Dialysis system having regimen generation methodology

ABSTRACT

A peritoneal dialysis system includes a logic implementer configured to: (i) accept at least one therapy target input; (ii) accept at least one patient transport characteristic input; (iii) accept at least one solution input; (iv) accept at least one fluid volume input; (v) accept at least one therapy time input, at least one of the inputs (ii) to (v) including an input range; and (vi) generate each therapy regimen using the inputs of (ii), (iii), (iv) and (v), including each of the possibilities within the at least one input range, that satisfy the at least one therapy target input.

BACKGROUND

The proportion of patients performing automated peritoneal dialysis(“APD”) is increasing worldwide, which is due in part to the ability ofAPD to be adapted to the patient's particular needs regarding thepatient's private life and the patient's therapy needs. The two primarygoals of dialysis, solute clearance and ultrafiltration (“UF”) depend onthe modality or type of APD performed (e.g., nocturnal intermittentperitoneal dialysis (“NIPD”), continuous cycling peritoneal dialysis(“CCPD”) and hi-dose CCPD, solution type, therapy time and fill volume.Prescribing an APD therapy constitutes selecting one of each of these.Thus there are many combinations and possibilities from which to choose.

APD devices typically do not have the capability to provide feedback tothe patient regarding the effectiveness of his/her recent therapies.Also, APD devices typically run open loop such that they do not adjusttherapy parameters (e.g., modality, solution type, therapy time and fillvolume) based on the actual measured daily clearance and UF.Accordingly, some patients underachieve their targets and developadverse conditions such as fluid overload and in some caseshypertension. Current methods for adjusting treatment typically involvethe patient reporting to a center every so often to be evaluated. Thesemethods place the burden of therapy adjustment solely on the doctor orclinician and do not occur frequently enough to adjust properly to thepatient's weekly, monthly, seasonal or other lifestyle change.

The systems and methods of the present disclosure attempt to remedy theabove problems.

SUMMARY

The system of the present disclosure includes a plurality ofprescription optimization modules. One of the modules is an advancedperitoneal equilibration test or (“PET”). The PET allows the patient toperform an ultrafiltration (“UF”) portion of the PET at homeautomatically using the patient's automated peritoneal dialysis (“APD”)machine. The automated test collects more UF data point samples than inthe past, which helps to establish a more accurate UFpatient-characteristic curve. The UF samples and blood work are thenused to generate physiological data for the patient.

A second module of the prescription optimization system of the presentdisclosure is to use the above patient physiological data incontinuation with specified therapy targets (e.g., UF, clearance) andranges of therapy inputs (e.g., number of exchanges, cycle times,solution choices) to calculate or deliver all possible regimens that fitwithin the provided ranges.

A third aspect or module of the present disclosure is to use one or morefilter specifying a patient's preference or doctor's performancerequirement to narrow the number of regimens to a manageable few. Thedoctor or patient agree upon a certain number of the regimens, e.g.three to five regimens, to be prescribed by the doctor for treatment.

Another feature of the present disclosure is an inventory trackingmodule that tracks different solutions and other supplies used over adelivery cycle in performing the different prescriptions selected by thepatient. The APD machine has the ability to mix dialysis solutions ofdifferent dextrose levels to produce hybrid dextrose dialysate blends,which further enables the patient's treatment to be optimized. Theinventory tracking feature records dextrose levels used and replaces theassociated dialysate concentrations consumed.

A fifth module of the present disclosure is a trending of theprescriptions that the patients are performing. The trends can track anyone or more of UF removed, body weight blood pressure and prescriptionused. A sixth module controls how the different prescriptions arerecalled for use and adjusted or replaced if needed. The trends can alsotrend and show averages such as a seven day moving average UF and/or athirty day moving average.

In one embodiment, the patient at the beginning of treatment weighshimself/herself (e.g. after initial drain) and takes the patient's bloodpressure. As discussed in detail herein, the present system includes ascale and blood pressure taker that output weight and blood pressureinformation, respectively, wirelessly to the APD machine using awireless technology such as Bluetooth™, WiFi™, Zigbee™ technology. Thewireless link is provided via radio frequency transmission in oneembodiment. In an alternative embodiment, the weight and blood pressureinformation are provided via a wired connection between the scale andblood pressure taker and the APD machine. The patient alternativelytypes in the weight and blood data.

The APD machine provides multiple therapy prescriptions from whicheither the patient or the APD machine chooses one for that day'streatment. For example, the APD machine can provide five approvedprescriptions for the patient, one of which is chosen based on thepatient's weight, blood pressure and UF removed over the lasttwenty-four hours. The prescription does not need to be selected beforethe initial drain (initial drain is done in any of the possibletreatments), so that the patient or machine can obtain the final UFinformation from the previous day's treatment before determining whichprescription to select. The machine in one embodiment provides promptsat the beginning of treatment to remind the patient to obtain thepatient's weight and blood pressure.

The five possible prescriptions are determined analytically. Onepossibility is to develop and store an algorithm either in the APDmachine itself or in a server computer that communicates with to the APDmachine, e.g., via an internet connection. The algorithm can employ athree-pore model, for example, which adds predicted clearances acrossthree different sized pores in the patient's peritoneal membrane. Flowof toxins through large pores, small pores and micro-pores in the bloodvessels of the peritoneal membrane are summed. Urea and creatinine forexample are removed from the patient's blood through the small pores.The large pores allow larger protein molecules to pass from thepatient's blood to the dialysate. In each case, an osmotic gradientdrives the toxins from the patient's blood to the dialysate. Using thepredicted clearances and the three-pore model, the system of the presentdisclosure outputs a plurality of acceptable regimens for the patient.From these regimens five prescriptions can be selected, for example. Thealgorithm alternatively uses a two-pool model as discussed in detailbelow.

In one embodiment, the regimen generation portion of the system isstored and used at a dialysis center or nephrologist's office. Thepatient enters the office or center, and the doctor or clinicianexamines the patient, enters patient data into regimen generationsoftware located at the office or center, which outputs suitableregimens from which, e.g., five, different prescriptions are selected.These different prescriptions are expected to address the patient'svarying APD needs for the foreseeable future, e.g., over the next sixmonths to a year. New patients may have residual renal function (“RRF”),such that their kidneys are partially functional. These patients do notneed treatments that are as aggressive as do patient's with no kidneyfunction. The need for adjustment for these patients may be greaterhowever as RRF degrades. Here, the plurality of prescriptions may needto be upgraded more frequently, e.g., every three months.

The three-pore model is very good at predicting clearance values, suchas urea removed, Kt/V, pKt/V, creatinine removed, CCr, pCCr, glucoseabsorption and total effluent. The system of the present disclosure alsouses an advanced peritoneal effectiveness test (“PET”) to help predictUF removal accurately. The advanced PET also includes blood samples andchemical analysis performed on dialysate samples to accurately predictthe patient's clearances for urea removed, Kt/V, pKt/v, creatinineremoved, CCr, pCCr, glucose absorption and total effluent even better.

Regarding the advanced PET module of the present disclosure, theadvanced PET includes samples taken over two therapies. In the firsttherapy, for example, UF is measured after a thirty minute dwell, sixtyminute dwell, 120 minute dwell and 240 minute dwell, totaling 7.5 hours.In the second therapy, UF is measured after an eight hour dwell,providing a total of five data points. At each time interval, thepatient is drained completely to determine UF accurately. The UF can bemeasured by the APD machine or alternatively or additionally via aweight scale if more accuracy is desired. Because the patient has torefill after draining completely each time, the actual UF time periodsmay differ slightly from the intended UF time periods. For example, thetwo hour UF time period may actually consume two hours and ten minutes.The system of the present invention records the actual time period anduses the actual time as an input to the kinetic modeling.

The blood and dialysate samples can be taken at certain intervalswherever and whenever blood can be drawn. The first and second therapiescan be performed consecutively or over two nights. In one embodiment,the patient performs the half hour, one hour, and four hourfills/dwells/drains at night before bed. The patient then performs alast fill, sleeps and is awakened in time to perform the eight hourdrain. UF and actual dwell times are recorded for each dwell period. Thedata can be recorded on a data card that the patient beings the next dayto the lab or center. The data is transferred to the lab or centeralternatively via an internet using a data communications modulediscussed herein.

The patient then travels to a dialysis center. The patient is filled anadditional time. Blood and dialysate samples are taken at two hours andfour hours, for example. A second four-hour drain UF data point can betaken and compared to the first four-hour drain UF data point foradditional accuracy.

The five data UF points, blood testing and dialysate testing data areobtained using a particular dialysate concentrate, such as 2.5 percentdextrose. The data points generate mass transfer area coefficient(“MTAC”) data and hydraulic permeability data to classify the patient'stransport and UF characteristics, which can then be appliedphysiologically to other types of dialysate concentrate, such as 1.5percent dextrose and Extraneal® dialysate.

Regarding the regimen generation module of the present disclosure, theprediction algorithms use the above calculated patient transport and UFcharacteristics, target information and other therapy input informationto generate the regimens. The target and other therapy informationinclude for example: (i) clinical targets such as (a) minimum ureaclearance, (b) minimum urea Kt/V, (c) minimum creatinine clearance, (d)maximum glucose absorption, and (e) target UF; and (ii) therapyparameters such as (a) therapy time, (b) therapy volume, (c) fillvolume, (d) percent dextrose, and (e) solution type; and (iii) availablesolutions. Many of these inputs are expressed in ranges, leading to manypossible regimen outcomes. The software using the three-pore or two-poolmodel equations generates, in one embodiment, every possible regimenusing (i) each input in each range and (ii) the patient's transport andUF characteristics, which meets the above-described target and therapyinformation.

Regarding the prescription filtering module, the system allows the userto specify certain filtering information to pair the regimencombinations down to a manageable number of combinations from which toselect and approve as set of prescriptions. The filtering informationcan include for example filtering out all regimens with aKt/V,creatinine clearance and UF below a certain value, and a glucoseabsorbed above a certain value. The clinician and patient then agree ona few of the paired down prescription possibilities to be stored asprescriptions on the patient's APD machine or alternatively on a servercomputer having a communications link to the APD machine.

The prescriptions can be stored on the patient's data card and loadedphysically into the APD machine when the patient returns home.Alternatively, a dialysis center downloads the prescriptions to the APDmachine via a internet or other data communications network. One set ofprescriptions can include for example: (i) a standard UF prescription;(ii) a high UF prescription; and (iii) a low UF prescription. If thepatient is capable, the APD system can be set to allow the patient torun a prescription tailored to the day's activities. If the patientexercises heavily on certain days, losing large amounts of body fluidsvia sweat, the patient can run a lower UF treatment that evening,perhaps saving the patient from having to perform a day exchange thenext day. If the patient is out to dinner on a given night and consumesmore liquids than normal, the patient can run the high UF treatment. Onall other days the patient runs the standard UF prescription.

One corollary component to the instant system is the inventorydistribution and tracking module. If the selected prescriptions requiredifferent types of dialysate concentrates, the different dialysate typesneed to be delivered in the right quantity. In the present system,either the APD machine or the clinician's server tracks the patient'scurrent inventory and ensures that a needed supply of the appropriatesolutions is delivered to the patient's home.

As discussed herein, one of the therapy parameters is percent dextroseof the solution, which affects the amount of UF removed and the amountof calories absorbed by the patient. More dextrose results in more UFremoved, which is generally desirable. More dextrose, however, resultsin more caloric intake and weight gain by the patient, which is notdesirable. Dextrose profiling is therefore an important factor inselecting the patient's possible therapies. The different solutions areprovided in different dextrose percentages, such as 1.5%, 2.5% and 4.25%dextrose. The APD machine described in connection with the presentsystem has the ability to connect to multiple solution bags havingdifferent glucose percentages and pull solution from the different bagsto form a mixed or blended solution, e.g., in a warmer bag or in thepatient's peritoneum if inline heating is used, having a desired glucosepercentage different from the rated percentages, e.g., 2.0%, 2.88%,3.38% and 3.81% dextrose. If mixed in a warmer bag, the warmer bag canbe fitted with one or more conductive strip, which allows a temperaturecompensated conductivity reading of the mixed solution to be taken toensure that the solutions have been mixed properly.

In one embodiment, the APD machine tracks the different bags used duringtreatment over the course of a delivery cycle. Before the deliveryperson arrives with new solution bags, the inventory used is sent to thesolution distribution facility. This inventory is subtracted from thepatient's total home inventory, so that the solution distributionfacility knows how much of which solution(s) to deliver to the patient'shome. In one embodiment, when the delivery person arrives at thepatient's home, the delivery person scans in the patient's remaininginventory for comparison with what is expected to be remaining. The APDmachine does not automatically account for solution bags that aredestroyed, lost or otherwise not used for treatment. It is contemplatedhowever to allow the patient to enter a lost or destroyed bag amount (oractual total amount of bags remaining) into the APD machine trackingsystem if such procedure is found to provide more accurate advanceinventory information. The delivery person's scan of the remainingproduct in any case resets the patient's inventory at each delivery sothat errors do not accumulate.

Regarding the trending and alert generation module, the APD system ofthe present disclosure tracks or trends certain therapy parameters, suchas daily UF, blood pressure and body weight. The trends in oneembodiment can be seen on the display device of the APD machine betweentherapies, for example at the beginning of a therapy. The trends canalso be viewed by the clinician and/or doctor. The patient at thebeginning of therapy can for example view the patient's weight, bloodpressure and UF removed over the last twenty-four hours. The patient canlearn of a previous day's UF after the patient's initial drain, which isfrom the last fill of the previous night's therapy. That is, the end ofa first initial drain to the end of a second initial drain sets a UFcycle that is recorded. The patient after the initial drain weighshimself/herself, providing data that is entered into the machine viacable or wirelessly or, alternatively, via patient entry. The trend datain combination with a set of filters or criterion are used to eitherallow treatment to be performed under a current set of prescriptions orto be run via a new prescription(s). The machine in one embodimentautomatically alerts the clinician if an alarm condition occurs or if aprescription change is needed.

The trend screens can show for example a daily UF trend, a seven daymoving average trend and a thirty day moving average trend. The movingaverage trends smooth the daily highs and lows and more readily indicateUF up and down trends. When the actual UF removed falls below a lowthreshold UF removed, e.g., over a predefined period of time, or incombination with the patient running a high UF prescription to avoidunderperforming UF, the system of the present disclosure causes the APDmachine to send an alarm or alert signal to the dialysis center and/ordoctor. Different methods or algorithms are discussed herein forpreventing the system from overreaction or being too sensitive. Besidesthe rolling UF averages, the system also uses algorithms that accumulateerror and look for other physiological patient parameters, such asweight and blood pressure in an attempt to view the patient moregenerally instead of just looking at UF removal.

Regarding the prescription recall and adjustment module, the clinicianor doctor responds to the prescription alarm in an appropriate manner,such as by calling the patient into the clinic, making suggestions viaphone or email regarding the use of the currently existingprescriptions, ordering a new PET, and/or changing the patient'sprescriptions. The system is thus configured to remove the burden ofdetecting a poor performing prescription(s) from the clinician or doctorand to alert of such poor performing prescription as soon as reasonablypossible without overreacting to one or more underachieving day.

If an alert condition occurs and the APD machine is not able tocommunicate the alert condition to the clinic or center, e.g., nointernet service is available or accessible, the system is configured tohave the APD machine prompt the patient to contact the clinic or center.To that end, the system includes a handshaking procedure in which theclinician's server sends an electronic receipt-confirmed message to theAPD machine, so that the APD knows that the alert has been received.

The APD system can be set to respond in a closed-loop manner to aconsistent underperforming therapy. Here the APD machine stores adatabase of acceptable prescriptions, e.g., the five prescriptionsprescribed above by the doctor. The machine automatically selects aprescription approved by the doctor in an attempt to improve results.For example, the machine could select a higher UF prescription if thepatient's UF has been underperforming. Or, the machine presents a numberof prescription options to the patient, who selects one or moreprescription approval by the doctor. It should be appreciated that inone embodiment, the system only allows the patient to run a prescriptionapproved by a doctor.

In another alternative embodiment, the plurality of, e.g., five,approved prescriptions are loaded onto the patient's APD machine butonly a subset, e.g., three of five, prescriptions is enabled. When theenabled treatments are found to be underperforming, a doctor orclinician enables a previously non-enabled therapy that is, for example,capable of increasing the patient's UF. In this way, the doctor orclinician can select from a pre-approved set of prescriptions and nothave to rerun the regimen generation and prescription filteringprescription sequence described above. The system can also provide theabove-described alert responses for blood pressure and body weight alertconditions.

The system can respond to alerts in other ways besides changing thepatient's prescription. The trends show which prescriptions have beenrun on which days, so the doctor or clinician can look to see if thepatient is running enough therapies and/or the right therapies. Thetrends also show patient weight, allowing the doctor or clinician torecommend that the patient reduce his/her food and drink intake if thepatient has gained too much weight. Patient blood pressure can also betrended and considered upon an alert. Discussed herein are a pluralityof algorithms and examples illustrating possible repercussions from apatient alert.

When the patient's therapy is functioning properly, the system canoperate to allow the patient to select a prescription to run on a givenday. The machine or system can alternatively select the dailyprescriptions to run, or some combination thereof. Discussed herein area number of prescription adjustment algorithms or methods that attemptto balance patient flexibility and lifestyle concerns with therapyperformance concerns.

It is accordingly an advantage of the present disclosure to provide anAPD system that analyzes patient treatment data and communicates resultsto the doctor or clinician, who can then spend more time talking to thepatient rather than performing the analysis manually.

It is another advantage of the present disclosure to provide aperitoneal dialysis system that attempts to optimize a therapyprescription for the patient.

Still further, it is an advantage of the system of the presentdisclosure to track or trend various patient PD physiologicalparameters, to provide rolling averages of same, and to react quicklybut not overreact to an apparent underperforming therapy.

It is a further advantage of the system of the present disclosure toprovide a closed-loop PD system that responds to underperforming UF,patient weight gain and/or high blood pressure.

It is yet another advantage of the system of the present disclosure toprovide a system that tracks patient solution use to deliver multipleneeded solutions to the patient efficiently.

It is still a further advantage of the system of the present disclosureto provide a dialysis system that mixes standard dextrose solutions toachieve a desired, blended dextrose level dialysate.

It is yet a further advantage of the system of the present disclosure toprovide a dialysis system having a plurality of approved prescriptionsfrom which the patient can choose to fit a patient's daily needs.

It is still another advantage of the system of the present disclosure toprovide a dialysis system that uses a more accurate peritonealequilibration test.

Additional features and advantages are described herein, and will beapparent from, the following Detailed Description and the figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic view of one embodiment of the prescriptionoptimization system of the present disclosure including an improvedperitoneal equilibration test (“PET”) module, regimen generation module,prescription filtering and selection module, dialysis fluid inventorymanagement module, communications module, data input module, trendingand alert generation module, and a prescription recall and adjustmentmodule.

FIG. 2A is a perspective view of a peritoneal blood vessel showing thedifferent sizes of pores used in a three-pore model for predictingperitoneal dialysis therapy results.

FIG. 2B is a schematic view of the kinetic transport properties of atwo-pool model used for predicting peritoneal dialysis therapy results.

FIG. 3 illustrates a sample plot of UF removed versus dwell time fromdata collected by the dialysis instrument at home for use with animproved peritoneal equilibration test (“PET”) of the presentdisclosure.

FIGS. 4A to 4D are sample screens displayed on a clinician's or doctor'scomputer as part of the system of the present disclosure, illustratingadditional data associated with the improved PET module.

FIG. 5 is a sample screen displayed on a clinician's or doctor'scomputer as part of the system of the present disclosure, illustratingdata associated with the regimen generation module.

FIGS. 6A to 6C are sample screens displayed on a clinician's or doctor'scomputer as part of the system of the present disclosure, illustratingfiltering criteria used to filter the generated regimens into possibleprescriptions for therapy.

FIGS. 7A and 7B are sample screens displayed on a clinician's ordoctor's computer as part of the system of the present disclosure,illustrating the selection of prescriptions from the list of filteredregimens.

FIGS. 8A to 8C are sample screens displayed on a clinician's or doctor'scomputer as part of the system of the present disclosure, illustratingthe agreed upon high UF, standard UF and low UF prescriptions,respectively.

FIGS. 9A to 9E illustrate another example of a filtering processaccording to the present disclosure, which results in a final filter ofthree prescribed regimes for the patient.

FIGS. 10 to 13 are sample screens displayed on a clinician's or doctor'scomputer as part of the system of the present disclosure, illustratingone embodiment of an inventory management module.

FIG. 14 is a perspective view of one embodiment of a dialysis instrumentand disposable pumping cassette illustrating one suitable apparatus forperforming dextrose mixing used with the prescription optimizationsystem of the present disclosure.

FIGS. 15A and 15B are schematic views illustrating embodiments forwireless and wired communications modules, respectively, for theprescription optimization system of the present disclosure.

FIG. 16A is a schematic view illustrating another embodiment for acommunications module and an embodiment of the data collection module,which includes wireless weight and blood pressure data entry for theprescription optimization system of the present disclosure.

FIG. 16B is a schematic view illustrating a further embodiment for acommunications module, in which a central clinical server is located ator associated with a particular dialysis center.

FIGS. 17 to 21 are sample screens displayed on a patient's dialysisinstrument illustrating various trending data available to the patient.

FIGS. 22 and 23 are sample screens displayed on a patient's dialysisinstrument or a clinician's and/or doctor's computer as part of thetrending module of the present disclosure, illustrating various trendingdata including moving UF averages, target UF, daily UF, UF limits andprescription used.

FIG. 24 is a schematic diagram illustrating one possible alertgeneration algorithm for the trending and alert generation module of theprescription optimization system of the present disclosure.

FIG. 25 is a logic flow diagram illustrating another possible alertgeneration/prescription modification algorithm for the trending andalert generation module of the prescription optimization system of thepresent disclosure.

FIG. 26 is a sample screen displayed on a patient's dialysis instrumentor a clinician's and/or doctor's computer as part of the system of thepresent disclosure, illustrating various trending data including movingUF averages and UF limits determined via statistical process control.

FIG. 27 is a sample screen displayed on a patient's dialysis instrumentor a clinician's and/or doctor's computer as part of the system of thepresent disclosure, illustrating various trending data including movingUF averages, target UF, prescription used and UF limits determined viastatistical process control.

FIG. 28 is a schematic view of one embodiment of a transfer functionused for the prescription recall and adjustment module of theprescription optimization system of the present disclosure.

FIG. 29 is a sample screen displayed on a patient's dialysis instrumentillustrating one possible prescription recall embodiment of theprescription recall and adjustment module of the prescriptionoptimization system of the present disclosure.

DETAILED DESCRIPTION

Referring now to the drawings and in particular to FIG. 1, a schematicview of peritoneal dialysis (“PD”) system 10 having an automatedperitoneal dialysis (“APD”) machine 104 is illustrated. System 10provides and uses an improved peritoneal equilibration test (“PET”) 12,which samples and employs multiple data points for determining thepatient's ultrafiltration (“UF”) curve over the course of treatment. PET12 improves UF prediction used to characterize an individual's responseto PD therapies and helps the clinician to generate optimizedprescriptions for patients. In one embodiment, PET 12 is performed as afixed therapy using the APD machine 104 at home. PET 12 also requireslab testing and analysis as set forth below.

System 10 also performs automated regimen generation 14. Known regimengeneration is performed manually by a physician 110 or clinician 120using a hit or miss type strategy, which is time consuming and relies onscientific guessing by the nurse or physician. Automated regimengeneration feature 14 uses the results of PET 12, therapy inputparameters and inputted therapy target parameters to generate regimensfor the physician 110 or clinician 120, which saves time and increasesthe likelihood that one or more regimen is generated that meets thetherapy targets for clearance and UF, minimizes glucose exposure, andmeets lifestyle needs.

System 10 further includes a prescription filtering module 16, whichreduces the many regimens generated by automated regimen generationfeature 14 to a manageable number of prescriptions, which are thenselected by the patient and doctor/clinician to provide a set ofapproved prescriptions that are loaded onto the patient's APD machine104 at home. In one embodiment, the APD machine 104 supports up to fiveprescriptions that are transferred to the APD machine 104 via a datacard, the internet or other type of data communication. The differentprescriptions can include for example: two primary (or standard)prescriptions, one volume depleted (low UF) prescription, and two fluidoverloaded (high UF) prescriptions, totaling five. Not all of theprescriptions have to be enabled by the physician. However, once enabledby physician, the multiple prescriptions provide flexibility to thepatient and allow the APD machine 104 and therapy to better fit thepatient's life style needs, while providing a proper therapy.

Prescription filtering 16 naturally leads to an inventory trackingfeature or module 18. Different prescriptions can require the patient tostore different types of PD solutions at home. For example, one type ofsolution may be used for nighttime exchanges, while a second type ofsolution is used for a last fill or day exchange. Also, the same type ofsolution can be provided in different dextrose quantities. System 10determines what types and varieties of type of solutions are needed,quantities of such types and varieties, and what related disposablecomponents are needed to carry out the enabled prescriptions. The APDmachine 104 tracks how many of which type and variety of solutions areused over the course of a delivery cycle and communicates the usage tothe clinician's server. The clinician's server then determines how muchof which supplies need to be delivered to the patient for the nextdelivery cycle. When the delivery person arrives at the patient's house,he or she can scan the patient's actual remaining inventory to compareagainst the clinician server's expected remaining inventory. Thepatient's delivery can be adjusted if needed. Here, the patient's postdelivery inventory is known and sent to the clinician's server.Communication is performed using a communications module discussedbelow.

System 10 as mentioned includes a prescription download and therapy dataupload communications module 20 that transfers data between the APDsoftware and doctor/clinician's software, e.g., via any one or more ofthe internet, modem and cell phone. The dialysis center could usecommunications module 20 for example to send updated prescriptions tothe patient's APD machine. Therapy data, logs, trending data can beuploaded to the doctor/clinician's data center, so that the physician orclinician can access patient information at any time and from anywhere.

System 10 also includes an automated, e.g., twenty-four hour UF, bloodpressure and body weight data collection feature 22. The APD machinedetermines patient twenty-four hour UF, and obtains blood pressure andbody weight daily and automatically in one embodiment. A remote exchangesystem (“RES”) collects the patient's mid-day exchange data and feedssuch data to the APD machine daily, e.g., via bluetooth or otherwireless communication. Blood pressure and body weight devices can alsocommunicate the patient's blood pressure and body weight to the APDmachine, e.g., daily and wirelessly. Data collection feature 22 alsoincludes the collection and input of therapy ranges and targetinformation, e.g., into regimen generation feature 14.

System 10 further includes a trending and alert generation feature 24.The APD machine provides, for example, up to ninety days of trending oftwenty-four hour UF, blood pressure, heart rate, body weight andprescription used. The patient, clinician and/or doctor can view thesecurves on the display of the APD machine, clinician computer or doctorcomputer. The APD machine obtains the data necessary for the trends,sends the data to a server computer, which in turn generates andmonitors the trends. The APD machine and/or clinical software monitorsthe patient therapy trending data and generates alerts when any of thevital parameters falls outside a physician's preset range (or outside ofthe range in combination with other alert filtering criteria describedherein).

System 10 further includes a prescription recall and modificationfeature 26. Based on the data the from trend feature 24, patient 102,doctor 110 and/or dialysis center 120 may recall one approvedprescription for daily use over another. For example, if the patient'sUF results for the past few days have been less than expected, thepatient 102, doctor 110 and/or dialysis center 120 may decide to use ahigher UF prescription as opposed to a standard UF prescription. System10 for example can store three or five different prescriptions, whichhave all been doctor approved. The five prescriptions may include, forexample, (i) low UF, (ii) standard UF with shorter duration and higherdextrose, (iii) standard UF with longer duration and lower dextrose,(iv) high UF with shorter duration and higher dextrose, and (v) high UFwith longer duration and lower dextrose.

If the patient 102, doctor 110 and/or dialysis center 120 knows that thepatient has gained weight of late, a lower dextrose prescription may beselected to reduce the caloric input from the treatment. Otherwise thepatient may wish to run a shorter therapy or one without a mid-dayexchange for life style reasons. System 10 can be configured such thatthe patient chooses which prescription to run on a given day.Alternatively, the dialysis instrument 104 runs a prescriptiondownloaded from the dialysis center 120. Further alternatively, thedialysis instrument 104 runs a prescription downloaded from the doctor110. System 10 can run a hybrid control, which for example allows thepatient to choose which prescription to run on a given day as long asthe patient is making responsible choices, and if the patient does notmake responsible choices, system 10 switches so that the prescription torun is set by the machine for the patient. Alternatively, if the patientdoes not make responsible choices, system 10 can, e.g., remove lessaggressive options from the list of possible prescriptions but stillallow the patient to choose from the remaining prescriptions.

Many PD patients lose residual renal function (“RRF”) over time, so thatthe PD therapy needs to remove more UF. Also, the patient's transportcharacteristics may diminish due to a loss of RRF. When trendingfunction 24 indicates that the patient's UF is underperforming no matterwhich prescription the patient runs, the patient is gaining too muchweight, the patient's blood pressure is too high, or a combination ofthese conditions occurs, system 10 according to module 26 willautomatically alert that the patient's prescriptions likely need to bemodified. Discussed herein are a number of measures taken so that system10 is not oversensitive and allows for natural fluctuations in patientUF, due for example to instrument error and residual volume of fluidleft in the patient's peritoneum. However, when the patient shows apattern of underperformance sufficient to indicate that it is not aresult of normal fluctuation, system 10 institutes a number ofprocedures to improve the patient's PD performance. For example, system10 can call for a new PET to be performed, new regimens to be generatedaccordingly, and new prescriptions to be filtered from the regimensgenerated. Or, perhaps as an initial attempt, system 10 calls for a newset of filtering criteria (e.g., more stringent therapy criteria) to beapplied to the previously generated regimens to filter out a new set ofprescriptions. The new prescriptions are downloaded to the patient'sdialysis instrument 104 via either a data memory card or via an internetlink from the doctor's office 110 or the dialysis clinic 120.

Peritoneal Equilibration Test (“PET”)

Referring now to FIG. 2A, a cross-section from a peritoneal blood vesselillustrates the three-pores of the vessel. The three-pores each havetheir own toxin and UF clearance, leading to one kinetic model calledthe three-pore model. The three-pore model is a mathematical model thatdescribes, correlates and predicts relationships among the time-courseof solution removal, fluid transfer, treatment variables andphysiological properties. The three-pore model is a predictive modelthat can be used for different types of dialysate, such as Dianeal®,Physioneal®, Nutrineal®, and Extraneal® dialysates marketed by theassignee of the present disclosure.

The three-pore model is based on an algorithm, which is described asfollows:

$\frac{V_{D}}{t} = {J_{V_{C}} + J_{V_{S}} + J_{V_{L}} - L}$

in which:

-   V_(D) is the peritoneal fluid volume;-   J_(VC) is the flow of fluid through transcellular pores or    aquaporins shown in FIG. 2A;-   J_(VS) is the flow of fluid through small pores shown in FIG. 2A;-   J_(VL) is the flow of fluid through large pores shown in FIG. 2A;    and-   L is the peritoneal lymph flow.

Research has shown the thee-pore model and the two-pool model areessentially equivalent in terms of UF and small solute clearance. Thetwo-pool model is easier to implement than the three-pore model becauseit requires less computation, and thus less computer time. Research alsohas shown that the correlation between predicted results derived fromthe prediction software versus actual results measured from actualtherapies have, for the most part, good correlation. Table 1 below showsresults of one study (E. Vonesh et al., 1999). Correlations (rc) areaccurate for urea removed, weekly urea clearance (pKt/V), total ureaclearance (Kt/V), creatinine removed, weekly creatinnine clearance(pCCr), total creatinine clearance (CCr), glucose absorption and totaleffluent (drain volume). The UF correlation however is not as accuratedue possibly to: APD device UF volumetric accuracy, residual solutionvolume in the patient, variation of patient's transport characteristics,and limited input points for estimating key kinetic parameters.

TABLE 1 Correlation Between Kinetic Software Model And Actual MeasuredResults Three-Pole Model (PD Adequest 2.0 with APD, n = 63) MeasuredPredicted Outcome measure Mean SD Mean SD r_(c) Urea removed (g/day)5.33 1.86 5.35 1.88 0.93 Kt/V 2.25 0.44 2.23 0.49 0.83 pKt/V 1.93 0.561.94 0.59 0.89 Creatinine removed (g/day) 0.72 0.31 0.72 0.31 0.93 CCr(L/week/1.73 m2) 62.89 16.11 61.64 16.26 0.87 pCCr (L/week/1.73 m2)47.38 15.56 47.32 15.03 0.86 Glucose absorption (g/day) 103.1 61.57105.9 54.05 0.9 Total effluent (L/day) 14.47 4.83 14.57 0.509 0.98 Netultrafiltration (L/day) 0.983 0.672 1.09 0.784 0.23

It has been found that the certain APD devices can measure fill anddrain fluid volumes very accurately. For example, theHomeChoice®/HomeChoicePRO® APD machine provided by the assignee of thepresent disclosure has a reported total fill and drain volume accuracyof 1% or ±10 mL. An APD machine employing multiple exchange cyclesincreases the data points needed to estimate key kinetic parameters, andat the same time, reduces the possibility of introducing errors due tothe residual solution volume in the patient. A new PET is accordinglyproposed to improve the UF prediction accuracy, while maintaining orimproving the current good prediction of small solutes (or toxins).

FIG. 2B illustrates an alternative (two-pool PD) kinetic model thatsystem 10 can use for PET 12. The two-pool PD kinetic model of FIG. 2B,like that of FIG. 2A, is used to predict fluid and solute removal in PDto: (i) aid clinicians in the care and management of patients; (ii)assist clinicians in the understanding of the physiological mechanismsthat govern peritoneal transport; and (iii) simulate a therapy outcome.The set of differential equations that collectively describe bothdiffusive and convective mass transport in both the body and dialysatecompartments for an “equivalent” membrane core are as follows:

Body Compartment

d(V _(B) C _(B))/dt=g−K _(PA)(C _(B) −C _(D))−Q _(U) s C−K _(R) C _(B)

Dialysate Compartment

d(V _(D) C _(D))/dt=K _(PA)(C _(B) −C _(D))+Q _(U) s C

The diffusive mass transfer rate is the product of the mass transferarea coefficient, K_(PA), and the concentrate gradient, (C_(B)−C_(D)).K_(PA) is in turn equal to the product of the solute membranepermeability (p) and transfer area (A). The convective mass transferrate is the product of the net water removal (UF) rate, Q_(U), thesolute sieving coefficient, s, and the mean membrane soluteconcentration, C. K_(R) is the renal residual function coefficient.

Solving the above equations for the dialysis volume, V_(D), at time t isas follows:

Dialysate Volume

$V_{D} = {V_{D}^{1}\left\{ {1 + {1.5\; L_{PA}^{\prime}{\sum\limits_{i = 1}^{m}{{K_{i}^{*{- 1}}\left( {1 - s_{i}} \right)}\left( {C_{D,i}^{1} - C_{B,i}^{1}} \right)\left( ^{{- K_{i}^{*}}{t/V_{D}^{1}}} \right)}}}} \right\}^{2/3}^{{- Q_{L}^{0}}{t/V_{D}^{1}}}}$

in which (i) V_(D) ¹ is the dialysate volume immediately after infusion(mL); (ii) L_(PA)′ is the hydraulic permeability transport rate(mL/min/mmol/L); (iii) K_(i)* is the ith solutes value of mass transferarea coefficient (mL/min); (iv) S_(i) is the ith solutes sievingcoefficient; (V) C_(D,i) ¹ is the ith solutes dialysate concentrationimmediately after infusion (mmol/L); (vi) C_(B,i) ¹ is the ith solutesblood concentration immediately after infusion (mmol/L); (vii) t is thetime (min); and (viii) Q_(L) ⁰ is the lymphatic absorption (ml/min). UFcan accordingly be calculated from the above equation knowing infusionvolume, solution concentration and dwell time.

To estimate hydraulic permeability (LPA, mL/min/mmol/L) and lymphaticflow rate, (Q_(L), mL/min) for the above equation, two V_(D) values atcorresponding dwell time t1, and t2 are needed. The V_(D) (fillvolume+UF) value is difficult to measure due to the incomplete drain(cycler) and resulting UF measurement errors. PET 12 as shown in FIG. 3uses multiple, e.g., five, dwell volume (V_(D)) measurements at multiple(five) different corresponding dwell times, e.g., overnight, four-hour,two-hour, one-hour and half-hour, to improve the accuracy of L_(PA) andQ_(L) estimation and thus improve the UF prediction accuracy.

In one embodiment, the PET 12 of the present disclosure begins with a UFversus dwell time evaluation performed over the course of two treatments(e.g., two evenings or back to back) by directly estimating thepatient's fluid transport parameters and correlating the measuredparameters with other PET results. UF removed for a particular type ofdialysate is measured by filling the patient with fresh dialysate,allowing the solution to dwell within the patient's peritoneum for aprescribed time, draining the patient, and subtracting the fill volumefrom the drain volume to determine a UF volume for that particular dwelltime.

In one implementation, on a first night, using a standard dialysate,such as a 2.5% dextrose Dianeal® dialysate, the APD machine runs fourseparate two liter fill/drain cycles: a first cycle at a thirty minutedwell; second cycle at a sixty minute (one hour) dwell; third cycle at a120 minute (two hour) dwell; and a fourth cycle at a 240 minute (fourhour) dwell. Total of all dwell times is about seven hours, thirtyminutes, which including the time needed for filling and drainingconsumes about a typical eight hour total therapy time. The APD machinerecords fill volume, drain volume and actual dwell time for each cycle.The fill volume may be slightly less or more than two liters dependingfor example on how much fresh dialysate is actually existing initiallyin the bag and how empty the APD machine is able to make the bag. In anycase, the fill and drain volumes are recorded accurately so that theresulting calculated UF is also accurate.

In an alternative embodiment, to increase accuracy the patient weighsthe dialysate bag before fill and after drain. The weight values can besent wirelessly from the scale to the APD machine (as discussed indetail below). The patient alternatively enters weight data manually.The APD machine subtracts the pre-fill weight from the post-drain weightto accurately determine a UF value, which is matched with the actualdwell time.

The dwell time can be: (i) the time between the end of a fill and thebeginning of the corresponding drain; (ii) the time between thebeginning of a fill and the end of the corresponding drain; and (iii) inone preferred embodiment the time between the end of a fill and the endof the corresponding drain. In any of the scenarios, the actual dwelltime will likely be slightly more or less than the prescribed dwelltime. For example, in scenarios (ii) and (iii), a kinked line duringdrain will lengthen drain time and thus recorded dwell time. In scenario(ii) a kinked line during fill will lengthen fill time and thus recordeddwell time. The APD machine records the actual dwell times for use asshown below. The actual, not the prescribed times, are used so that anydifference between actual and prescribed dwell times does not introduceerror into the UF predictive model.

On the next or second night, using the standard (e.g., 2.5% dextroseDianeal®) dialysate, the APD machine patient runs a single fill volumewith a 480 minute or eight hour dwell. The APD machine records actualdwell time (according to any of the scenarios (i) to (iii) above) andmatches the actual dwell time with the actual UF recorded (e.g., via theAPD or weigh scale) in the APD machine.

At the end of two days, the APD machine has recorded five UF/dwell timedata points (more or less data points could be achieved, however, theabove five dwell times are acceptable and achievable over two standardeight hour therapies). In one embodiment, the APD machine sends theUF/dwell time data points to a server computer located at a dialysisclinic 120 or doctor's office 110 (FIGS. 15A, 15B, 16A and 16B) at whichthe remainder of the PET 12 is performed. Various embodiments forlinking the APD machine to a server computer are shown herein, forexample, an electronic mail link can be used to transport data. Inanother embodiment, the APD machine records the five (or other number)data points onto a patient data card that the patient inserts into theAPD machine. The patient then brings the data card and accompanying datato the dialysis center 120 or doctor's office 110 to complete PET 12.

The patient then travels to the dialysis center or doctor's office,e.g., the next day. The patient is filled an additional time and drainedtypically after four hours. Blood and dialysate samples are taken forexample at two hours and four hours. A second four-hour dwell UF datapoint can be taken and compared to the first four-hour dwell UF datapoint for additional accuracy. Alternatively, the second blood sample istaken at four hours but the patient is drained at, e.g., five hours,providing an additional UF dwell data point.

FIG. 3 illustrates a plot of the UF data points for the different actualdwell times. The server computer, or other clinical software computer isprogrammed to fit a curve 30 to the five data points. Curve 30 fills thegaps between the different recorded dwell periods (e.g., at 0.5 hour,one hour, two hours, four hours and eight hours) and thus predicts theUF that will be removed for any dwell period within the eight hour rangeand beyond, and for the particular dialysate and dextrose level used.

The osmotic gradient created by the dextrose in the dialysis solutiondecreases with time as dextrose is absorbed by the body. The patient'sultrafiltration rate accordingly begins at a high level and decreasesover time to a point at which the rate actually becomes negative, suchthat the patient's body begins to reabsorb fluid. Thus UF volume asshown in the graph can actually decrease after a certain dwell time. Oneof the goals of the present disclosure is to learn the patient's optimalUF dwell time, which may be dextrose level dependent, and incorporatethe optimal dwell time(s) into the prescriptions discussed in detailbelow.

In the illustrated embodiment, curve 30 predicts UF removed for 2.5percent dextrose. Once the curve is fitted for a particular patient, thecurve can then be calculated using the kinetic model to predict UF/dwelltime values for other dialysates and other dextrose levels, e.g., for1.5 percent and 4.25 percent dextrose levels. As shown in FIG. 3, for2.5 percent dextrose, curve 30 has a maximum UF removed dwell time ofabout three hundred minutes or five hours. Five hours is likely too longa dwell time, however, UF for a dwell time of two hours or 2.5 hourscomes fairly close to the maximum UF dwell time. A dwell time of twohours comes much closer to the maximum for 1.5 percent dextrose. 4.25percent dextrose lends itself to longer dwells as seen in FIG. 3. Forexample, a single day exchange is a good application for 4.25 percentdextrose.

Besides predicting optimal UF, the five or six UF data points, bloodtesting and dialysate testing data are taken using a particulardialysate, such as 2.5 percent dextrose Dianeal® dialysate. Each of theUF, blood and dialysate the data are used to generate mass transfer areacoefficient (“MTAC”) data and hydraulic permeability data to classifythe patient's transport and UF characteristics. The MTAC data andhydraulic permeability data can then also be applied physiologically toother types of dialysates, such as 1.5 or 4.5 percent dextrose solutionsand for different formulations, such as Extraneal® and Nutrineal®dialysates provided by the assignee of the present disclosure. That is,curve 30 is shown for one concentration. However, once the kinetic modeland LPA and QL (from PET test results) are known, system 10 couldcalculate the VD according to the algorithm above, for each the solutiontype, dextrose concentration, dwell time, and fill volume. An LPA valueof 1.0 (mL/min/mmol/L) and a QL value of 0.8 ml/min were used in thesimulation for curve 30 (2.5% dextrose) and the curves for 1.5% dextroseand 4.25% dextrose in FIG. 3.

A kinetic modeling simulation was conducted using the above algorithmfor dialysate volume V_(D), and the following data shown in Table 2 wasgenerated, which shows a comparison of UF estimation and associatederror using a known PET and PET 12. Data showed that the PET 12 improvedthe UF prediction accuracy significantly compared with a known PET.

TABLE 2 UF Prediction Accuracy Using PET 12 True UF with UF PredictionUF Prediction UF Prediction UF Prediction Dwell Time Dextrose of 2.5%Using Known PET Error Using Using PET 12 Error Using (min) (mL) (mL)Known PET (mL) PET 12 30 86.73 158.81 83.1% 111.48 28.5% 60 158.74281.52 77.3% 201.28 26.8% 90 217.88 374.37 71.8% 272.49 25.1% 120 265.71442.21 66.4% 327.68 23.3% 150 303.61 488.92 61.0% 368.98 21.5% 180332.76 517.70 55.6% 398.25 19.7% 210 354.20 531.24 50.0% 417.04 17.7%240 368.86 531.79 44.2% 426.72 15.7% 270 377.54 521.28 38.1% 428.4813.5% 300 380.95 501.35 31.6% 423.35 11.1% 330 379.73 473.44 24.7%412.23 8.6% 360 374.42 438.79 17.2% 395.92 5.7% 390 365.53 398.44 9.0%375.11 2.6% 420 353.49 353.34 0.0% 350.42 −0.9% 450 338.69 304.29 −10.2%322.39 −4.8% 480 321.48 252.00 −21.6% 291.49 −9.3% 510 302.16 197.07−34.8% 258.15 −14.6% 540 281.00 140.03 −50.2% 222.73 −20.7%

Automated Regimen Generation

As seen in FIG. 1, present system 10 includes a therapy regimengeneration module 14. Regimen generation module 14 includes a pluralityof prediction algorithms. The prediction algorithms use the abovecalculated patient transport and UF characteristics from PET 12, targetinformation and other therapy input information to generate theregimens. Regimen generation module 14 in one embodiment generates allof the possible therapy regimens that meet entered target requirementsusing the therapy input information and calculated patient transport andUF characteristics. The regimens generated are many as shown below. Theprescription generation module 16 then filters the regimens generated atmodule 14 to yield a finite number of optimized prescriptions that canbe performed on the APD machine for the particular patient.

FIG. 4A shows one data entry screen for regimen generation module 14.Data entered into the screen of FIG. 4A is obtained from PET 12. FIG. 4Aprovides PET 12 data inputs for the dialysis center clinical nurse. Thedata includes the dialysate UF measurement, dialysate lab test results(urea, creatinine and glucose), and blood test results (serum urea,creatinine and glucose). Specifically, an “Overnight Exchange” module ofFIG. 4A provides overnight exchange data input, including: (i) %dextrose, (ii) solution type, (iii) volume of solution infused, (iv)volume of solution drained, (v) dwell time(s), (vi) dialysate ureaconcentration (lab test result from drained dialysate), and (vii)dialysate creatinine concentration (lab test results from draineddialysate). A “Four-Hour Equilibration” module of FIG. 4A providesfour-hour exchange data input, which is normally obtained from a patientblood sample taken in a clinic, the data including: (i) % dextrose, (ii)solution type, (iii) volume of solution infused, (iv) volume of solutiondrained, (v) infusion time, and (vi) drain time. A “Data” module of FIG.4A provides four-hour exchange data input, including: (i) serum #1sample time (normally 120 minutes after infusion of the dialysate),urea, creatinine, and glucose concentration, which are the clinician'sinputs, “Corrected Crt” is a corrected creatinine concentration that thesoftware algorithm calculates; (ii), (iii) and (iv) for dialysate #1, #2and #3, sample time, urea, creatinine, and glucose concentration, whichare clinician's inputs, “Corrected Crt” and “CRT D/P” (dialysatecreatinine/plasma creatinine) which are calculated by software.

In FIG. 4B, the “serum concentration” module involves a blood test thatis typically performed after the regular APD therapy, preferably in themorning, Serum concentration (sometimes called plasma concentration) isthe lab test results of blood urea, creatinine and glucoseconcentration. A patient with end-stage kidney disease has blood ureaand creatinine levels that are much higher than for people withfunctioning kidneys. The glucose concentration is important because itmeasures how much glucose the patient's body absorbs when using adextrose-based solution. The “24-hour dialysate and urine collection”module of FIG. 4B shows that the patient has no residual renal function,thus produces no urine. The overnight collection data is used forpatient residual renal function (“RRF”) calculation, APD therapy results(fill, drain and lab test results), and for measuring the patient heightand weight to calculate the patient's body surface area (“BSA”). As seenin the example for the second day of PET 12, (eight hour dwell), 8000milliliters of dialysate was infused into the patient, 8950 millilitersof dialysate was removed from the patient, yielding a net UF volume of950 milliliters. The dialysate is sent to the lab for analysis of urea,creatinine, and glucose. A “weekly clearances” module calculates theweekly Urea Kt/V and weekly creatinine clearance (“CCL”), which areparameters that doctors use to analyze if patient has adequateclearance.

FIG. 4C of regimen generation feature 14 shows a sample screen in whichsystem 10 calculates mass transfer coefficients (“MTAC's”) and watertransport parameters using the data of screens 4A and 4B and storedalgorithms. RenalSoft™ software provided by the assignee of the presentinvention is one known software for calculating the data shown in FIG.4C from the input data of FIGS. 4A and 4B. Some of the data calculatedand shown in FIG. 4C is used in an algorithm for regimen generationfeature 14. Specifically, the regimen generation algorithm uses theMTAC's for urea, creatinine and glucose, and the hydraulic permeabilityto generate the regimens

FIG. 4D of regimen generation feature 14 shows the clinician or doctorthe predicted drain volumes determined based on the hydraulicpermeability of FIG. 4C versus the actual UF measured from PET 12. Theresults are shown for an overnight exchange (e.g., night one or nighttwo of PET 12) and the four-hour dwell test performed at the dialysiscenter 120 or doctor's office 110. The difference between actual drainvolume UF and predicted drain volume is calculated so that the clinicianor doctor can view the accuracy of PET 12 and the prediction routines ofFIGS. 4A to 4D for drain volume and UF. To generate the predicted drainvolumes, the operator enters a fluid absorption index. The machine alsocalculates a fluid absorption value used in the prediction of drainvolume. As seen in FIG. 4D, the software of system 10 has calculated thefluid absorption rate to be 0.1 ml/min based on values entered from thepatient's PET. As seen at the top of FIG. 4D, the actual versuspredicted drain volume for the overnight exchange was 2200 ml versus2184 ml. The four hour (day) predicted drain versus the actual drain was2179 ml versus 2186 ml. The bottom of FIG. 4D shows the actual versuspredicted drain values for a more common fluid absorption rate of 1.0ml/min, which are not as close to one another as those for the 0.1ml/min fluid absorption rate. The system can then ask the clinician toenter a fluid absorption rate that he/she would like to use for thispatient when predicting UF, which is normally somewhere between andincluding 0.1 ml/min and 1.0 ml/min.

FIG. 5 illustrates one possible regimen calculation input table forregimen generation feature 14. The regimen calculation input tableinputs Clinical Targets data including (a) minimum urea clearance, (b)minimum urea Kt/V, (c) minimum creatinine clearance, (d) maximum glucoseabsorption, and (e) target UF (e.g., over twenty-four hours).

The table of FIG. 5 also inputs Night Therapy Parameters data, such as(i) therapy time, (ii) total therapy volume, (iii) fill volume, (iv)percent dextrose for the chosen solution type, and possibly (v) solutiontype. Here, dwell times and number of exchanges are calculated from (ii)and (iii). Dwell time can alternatively be set according to the resultsof PET 12 as discussed above, which can be used in combination with atleast one other input, such as, total time, total volume and fill volumeto calculate the remainder of total time, total volume and fill volumeinputs. For example, if dwell time, total volume and total time are set,system 10 can calculate the fill volume per exchange and the number ofexchanges. The Night Therapy Parameters input also includes last fillinputs, such as (i) dwell time, (ii) last fill volume, and (iii) percentdextrose for the chosen solution type.

The table of FIG. 5 also inputs Day Therapy Parameters data, such as (i)therapy time, (ii) day fill volume, (iii) number of cycles, (iv) percentdextrose for the chosen solution type, and possibly (v) solution type.Day exchanges may or may not be performed.

Solution type for the night and day therapies is chosen from a Solutionsportion of the input table of FIG. 5, which inputs available dextroselevels, solution formulation (e.g., Dianeal®, Physioneal®, Nutrineal®,and Extraneal® dialysates marketed by the assignee of the presentdisclosure) and bag size. Bag size can be a weight concern especiallyfor elderly patients. Smaller bags may be needed for regimens that use adifferent last fill and/or day exchange solution or dextrose level.Smaller bags may also be needed for regimens that call for a dextroselevel that requires a mix from two or more bags of standard dextroselevel (e.g., dextrose level of 2.0%, 2.88%, 3.38%, or 3.81%) listedunder the Solutions portion of the table of FIG. 5. Mixing to producecustomized dextrose level is discussed below in connection with FIG. 14.Solution bag inventory management is also discussed below in connectionwith FIGS. 10 to 13.

The regimen calculation input table of FIG. 5 also illustrates that manyof the inputs have ranges, such as plus/minus ranges for the ClinicalTargets data and minimum/maximum/increment ranges for certain NightTherapy Parameters data and Day Therapy Parameters data. Once theclinician or doctor starts to complete the table of FIG. 5, system 10automatically places suggested values in many of the other cells,minimizing the amount of entries. For example, the solutions availableunder Solutions data can be filled automatically based upon thesolutions that have been indicated as available by the dialysis center,which is further based the available solution portfolio approved for aspecific country. In another example, system 10can adjust the fillvolume increments for Night Therapy Parameters data automatically to useall of the available solutions (e.g., when a twelve liter therapy isbeing evaluated, the number of cycles and the fill volumes can range asfollows: four-3000 mL fills, five-2400 mL fills, six-2000 mL fills, andseven-1710 mL fills). System 10 allows the operator to change any of thesuggested values if desired.

The software of system 10 calculates the predicted outcomes for all ofthe possible combinations of parameters based upon the ranges that areentered for each parameter. As seen in FIG. 5, some regimens will have“adequate” predicted outcomes for urea clearance, creatinine clearanceand UF that meet or exceed the minimum criteria selected by theclinician. Others will not. Selecting “Only Display Adequate Regimens”speeds the regimen generation process. Some patients may have hundredsof regimens that are adequate. Others may have only few, or possiblyeven none. When none are adequate, it may be necessary to display allregimens so that the regimens that are close to meeting the targetrequirements can be identified for filtering. Filtering when startingwith all regimens displayed provides the clinician/doctor the mostflexibility when trying to find the best prescription for the patient.

System 10 feeds all of the therapy combinations into outcome predictionsoftware and tabulates the results in a table that system 10 can thenfilter through as shown below in connection with prescription filteringmodule 16. One suitable software is RenalSoft™ software provided by theassignee of the present disclosure. The combinations take into accountthe different ranges entered above in the regimen calculation inputtable of FIG. 5.

Table 3 below shows the first ten results (of a total of 270 results)for one set of data inputted into system software. Here, a 1.5% nightdextrose solution is chosen. No day exchange is allowed. Results areshown generated for a standard PD therapy but could alternatively oradditionally be generated for other types of PD therapies, such as atidal therapy. FIG. 5 has a check box that allows either one or both ofcontinuous cycling peritoneal dialysis (“CCPD”) or tidal therapies to beincluded in the regimen generation process.

TABLE 3 APD Therapy Night Night Night Ther Ther Night Fill Number ofLast Fill Last Fill Last Fill Number of Day Fill Day Fill Day Fill DexTime Volume Volume Night Exch Volume Solution Dwell Time Day Exch VolumeSolution Dwell Time 1 1.5 7 8 2 4 0 0 0 0 0 0 0 2 1.5 7 8 2.2 3 0 0 0 00 0 0 3 1.5 7 8 2.4 3 0 0 0 0 0 0 0 4 1.5 7 8 2.6 3 0 0 0 0 0 0 0 5 1.57 8 2.8 3 0 0 0 0 0 0 0 6 1.5 7 8 3 2 0 0 0 0 0 0 0 7 1.5 7 9 2 4 0 0 00 0 0 0 8 1.5 7 9 2.2 4 0 0 0 0 0 0 0 9 1.5 7 9 2.4 3 0 0 0 0 0 0 0 101.5 7 9 2.6 3 0 0 0 0 0 0 0

As stated, Table 3 illustrates ten of two-hundred seventy validcombinations possible with a 1.5% night dextrose level. The sametwo-hundred seventy combinations will also exist for 2% dextrose level,2.5%, etc., night dextrose level. An equal number of valid combinationsis created for each possible dextrose level when a day fill is added.Further, the last fill dwell time can be varied to create even morevalid combinations.

Prescription Filtering

As shown above, system 10 allows the doctor/clinician to prescribevalues for clinical targets and therapy inputs, such as patient fillvolume, total therapy volume, total therapy time, etc., and generates atable, such as Table 3, containing all therapies that meet all of theclinical requirements. The table of therapies that meet all of theclinical requirements can then be automatically filtered and sortedbased upon parameters such as total night therapy time, therapy solutioncost, therapy weight, etc.

The software uses one or more algorithm in combination with the therapycombinations (e.g., of Table 3) and the patient physiologic datagenerated via PET 12 as shown in connection with FIGS. 4A to 4D todetermine predicted therapy results. Therapy combinations (e.g., ofTable 3) that meet the Clinical Targets of FIG. 5 are filtered andpresented to the clinician, nurse or doctor as candidates to becomeprescribed regimens.

FIGS. 6A and 6B illustrate examples of filters that the doctor orclinician can use to eliminate regimens. In FIG. 6A, minimum Urea Kt/Vremoval is increased from 1.5 in FIG. 5 to 1.7 in FIG. 6A. In oneembodiment, the range of +0 to −0.2 in FIG. 5 is applied automaticallyto the new minimum value set in FIG. 6A. Alternatively, system 10prompts the user to enter a new range or keep the same range. A Boolean“And” operator is applied to the new minimum Urea Kt/V to specify thatthe value must be met, subject to the applied range, in combination withthe other clinical targets.

In FIG. 6B, minimum twenty-four hour UF removal is increased from 1.0 inFIG. 5 to 1.5 in FIG. 6B. In one embodiment, the range of +0 to −0.2 inFIG. 5 is again applied automatically to the new minimum value set inFIG. 6B. Alternatively, system 10 prompts the user to enter a new dailyUF range or keep the same UF range. Again, a Boolean “And” operator isapplied to the new minimum twenty-four hour UF to specify that the valuemust be met, subject to the applied range, in combination with the otherclinical targets. The clinician and patient are free to imposeadditional, clinical and non clinical requirements, such as: solutioncost, solution bag weight, glucose absorbed, night therapy time, dayfill volume, night fill volume.

Referring now to FIG. 7A, the regimens that meet the Clinical Targetsand other inputs of FIG. 5 and the additional filtering of FIGS. 6A and6B are shown. As seen, each of the regimens is predicted to remove atleast 1.5 liters of UF per day and has a minimum Urea Kt/V removal ofgreater than 1.5.

Regimen 36 is highlighted because it has the smallest day fill volume(patient comfort), the lowest solution weight (patient convenience), andthe next to lowest glucose absorption value (least dietary impact).Thus, regimen 36 is chosen and prescribed by a doctor as a standard UFregimen.

The patient, clinician and/or doctor can also pick one or moreadditional prescription for approval that also meets with a patient'slifestyle needs. Suppose for example that the patient is a member of abowling team during the winter months that competes in a Saturday nightleague. He drinks a little more than normal while socializing. Thepatient and his doctor/clinician agree that a therapy regimen thatremoves about 20% more UF should therefore be performed on Saturdaynights. Also, on bowling nights, the patient only has seven hours toperform therapy rather a standard eight hour therapy. Filtered potentialprescription 34 highlighted in FIG. 7A is accordingly approved as ahigher UF prescription, which uses a higher dextrose concentration toremove the extra UF and does so in the required seven hours.

Further, suppose that the patient lives in a southern state and doesyard work on the weekends during the summer months (no bowling league)and accordingly loses a substantial amount of body fluid due toperspiration. The doctor/clinician and patient agree that less than 1.5liters of UF needs to be removed on such days. Because FIG. 8 only showsregimens that remove at or over 1.5 liters of UF, further filtering isused to provide low UF regimens for possible selection as a low UFprescription.

The doctor or clinician uses an additional filtering of the twenty-fourhour UF screen of FIG. 6C to restrict the prior range of daily UF thatthe regimes must meet. Here, the doctor/clinician looks for regimeshaving daily UF removals of greater than or equal to 1.1 liter and lessthan or equal to 1.3 liters. The Boolean “And” operator is selected suchthat the therapy meets all of the other clinical requirements of FIGS. 5and 6A.

Referring now to FIG. 7B, the regimens that meet the Clinical Targetsand other inputs of FIG. 5 and the additional filtering of FIGS. 6A and6C are shown. As seen, each of the regimens is predicted to remove at orbetween 1.1 and 1.3 liters of UF per day, while meeting the minimum UreaKt/V removal of greater than 1.5 and other clinical targets. Thedoctor/clinician and patient then decide on a tidal therapy regimen 58(highlighted), which does not require a day exchange, and requires theshortest night therapy time. The doctor then prescribes the therapy.

Referring now to FIGS. 8A to 8C, the three agreed-upon high UF, standardUF and low UF prescriptions are illustrated, respectively. Theprescriptions are named, so that they are easily recognizable by thepatient 102, doctor 110 and clinician 120. While three prescriptions areshown in this example, system 10 can store other suitable numbers ofprescriptions as discussed herein. System 10 downloads the prescriptionparameters onto a data card in one embodiment, which is then inserted inthe APD machine 104, transferring the prescriptions to the non-volatilememory of the APD machine. Alternatively, the prescriptions aretransferred to APD machine 104 via a wired data communications link,such as via the internet. In one embodiment, the patient is free tochoose which prescription is performed on any given day. In analternative embodiment, the data card or data link transfer of theprescriptions is entered into the memory of the dialysis instrument,such that the instrument runs a prescribed treatment each dayautomatically. These embodiments are discussed in detail below inconnection with the prescription recall and adjustment module 26. In anycase, when the patient starts any of the therapies, system 10 providesinstructions on which solution bags to connect to a disposable cassettefor pumping since the therapies may use different solutions.

FIGS. 9A to 9E illustrate another filtering example for module 16. FIG.9A illustrates filtered settings on the left and corresponding resultson the right, which yield 223 possible regimens. In FIG. 9B, theclinician further filters to regimens by limiting the day fill volume(patient comfort) to 1.5 liters. Such filtering reduces the possibleregimens to 59. In FIG. 9C, the clinical software further filter theregimens by decreasing the regimens to 19. In FIG. 9D, the clinicalfurther filters the available regimens by reducing the glucose absorbedto 500 Kcal/day. Such action reduces the available regimens to three.

FIG. 9E shows the three filtered regimens that can either be prescribedby the physician or discarded to enable another filtering exercise to beperformed. The exercise of FIGS. 9A to 9D shows that prescriptionoptimization is made convenient over the patient's physiologicalcharacteristics are known. FIGS. 9A to 9D also show are suitable list ofpossible filtering criteria.

Inventory Tracking

Referring now to FIGS. 10 to 13, one embodiment for an inventorytracking subsystem or module 18 of system 10 is illustrated. Asdiscussed herein, system 10 generates agreed-upon prescriptions, such ashigh UF, standard UF and low UF prescriptions as shown in connectionwith FIGS. 8A to 8C. The different prescriptions require differentsolutions as seen in FIG. 10. FIG. 10 shows that the standard UFprescription uses twelve liters of 1.5% Dianeal® dialysate and twoliters of Extraneal® dialysate. The high UF prescription uses fifteen toeighteen (depending on alternate dialysis used) liters of 1.5% Dianeal®dialysate and two liters of Extraneal® dialysate. The low UFprescription uses twelve liters of 1.5% Dianeal® dialysate and threeliters of 2.5% Dianeal® dialysate.

FIG. 11 shows an example screen that a server in a dialysis center 120can display for a patient having the above three prescriptions. Thescreen of FIG. 11 shows the minimum base supply inventory needed for thethree prescriptions over a delivery cycle. The screen of FIG. 11 showsthat the patient is to be supplied the solutions needed to performthirty-two standard UF prescription therapies. The patient is to besupplied the solutions needed to perform six high UF prescriptiontherapies. The patient is also to be supplied the solutions needed toperform six low UF prescription therapies. The patient is further to beprovided one “ultrabag” case (six 2.5 liter bags). The stem of aY-shaped ultrabag tubing set can connect to the patient's transfer set.An empty bag is attached to the one leg of the Y and a full bag ispre-attached to the other leg of the Y. The ultrabag is used to performCAPD exchanges if the APD machine breaks, if power is lost and the APDmachine cannot operate, or if the patient is traveling and his/hersupplies do not arrive on time.

Additionally, the patient is also to be provided with forty-fivedisposable sets (one used for each treatment), which includes adisposable pumping cassette, bag line, patient line, drain line, heaterbag and associated clamps and connectors. The patient is provided withlow (1.5%), medium (2.5%) and high (4.25%) dextrose concentrationdialysis solutions, so that the patient can remove more or less fluid byswitching which dextrose level is used. Prescription flexibility(including mixing) could increase the total number of bags needed for agiven month to about forty-five days worth of solutions.

The patient is also to be provided forty-five caps or flexicaps, whichare used to cap the patient's transfer set when the patient is notconnected to the cycler. The flexicaps contain a small amount ofpovidone iodine to minimize the potential for bacterial growth due totouch contamination.

FIG. 12 shows an example screen that the dialysis center 120 can displayfor a patient having the above three prescriptions. The screen of FIG.12 shows the expected actual patient inventory at the time a newdelivery of inventory is to be made. That is, the screen of FIG. 12shows what inventory the server computer thinks will be at the patient'shome when the delivery person arrives. In the example, the servercomputer expects that when the delivery person arrives at the patient'shome, the patient will already have: (i) five cases (two bags per case)of 1.5% Dianeal® dialysate containing six liter bags, (ii) one case(four bags per case) of 2.5% Dianeal® dialysate containing three literbags, (iii) one case (six bags per case) of Extraneal® dialysatecontaining two liter bags, (iv) twenty-four (out of a case of thirty)disposable sets, each including the apparatuses described above, (v) two1.5%, 2.5 liter Ultrabags of dialysate (out of a case of 6), and sixcaps or flexicaps (out of a case of thirty).

FIG. 13 shows an example screen that the server of dialysis center 120can display for a patient having the above three prescriptions. Thescreen of FIG. 13 shows the actual inventory that will be delivered tothe patient. That is, the screen of FIG. 13 shows the inventorydifference between what the patient is supposed to have at the start ofthe inventory cycle and what the server computer thinks will be at thepatient's home when the delivery person arrives. In the example, thepatient needs: (i) eighty-eight, six liter bags of 1.5% Dianeal®dialysate, has ten at home, reducing the need to seventy-eight bags orthirty-nine cases (two bags per case); (ii) six, three liter bags of2.5% Dianeal® dialysate, has four at home, reducing the need to two bagsor one case (four bags per case, resulting in two extra delivered);(iii) thirty-eight, two liter bags of Extraneal® dialysate, has six athome, reducing the need to thirty-two bags or six cases (six bags percase, resulting in four extra delivered); (iv) forty-five disposablesets, has twenty-four at home, reducing the need to twenty-one or onecase (thirty per case, resulting in nine extra disposable setsdelivered); (v) six, 2.5 liter 1.5% Ultrabags, has two at home, reducingthe need to four bags or one case (six bags per case, resulting in twoextra ultrabags delivered); and (vi) forty-five flexicaps, hasthirty-six at home, reducing the need to nine or one case (thirty-sixper case, resulting in twenty-seven extra flexicaps delivered).

In one embodiment, the dialysis center server database of the inventorytracking module 18 maintains and knows: (a) how much of each dialysateand other supplies the patient is supposed to have at the beginning of adelivery cycle; (b) the patient's different prescriptions, solutionsused with each, and the number of times each prescription is to be usedover the delivery cycle; and (c) accordingly how much of each dialysatethe patient is supposed to use over a delivery cycle. Knowing the above,the inventory tracking server can calculate how much of each solutionand other supplies needs to be delivered at the next delivery date.Chances are the patient has consumed more or less of one or moresolution or other item than expected. For example, the patient may havepunctured a solution bag, which then had to be discarded. The patientmay misplace a solution bag or other item. Both instances result in moreinventory being consumed than expected. On the other hand, the patientmay skip one or more treatment over the course of the delivery cycle,resulting in less inventory being consumed than expected.

In one embodiment, the inventory tracking module or subsystem 18 ofsystem 10 expects that the estimated amount of solutions and othersupplies is close to the actual amount needed. If too much inventory isdelivered (patient used less than prescribed), the delivery person candeliver the extra inventory to the patient and scan or otherwise notethe additional inventory, so that it can be saved into the inventoryserver memory. For the next cycle, system 10 updates (e.g., increases)(a) above, namely, how much of each dialysate and other supplies thepatient is supposed to have at the beginning of a delivery cycle.Alternatively, the delivery person only delivers the needed amount ofinventory, so as to make the patient's actual inventory at the beginningof the delivery cycle equal to the expected inventory at (a) above. Ifnot enough inventory is scheduled for delivery (patient lost or damagedsolution or related supplies for example), the delivery person bringsextra inventory so as to make the patient's actual inventory at thebeginning of the delivery cycle equal to the expected inventory at (a)above.

The inventory subsystem 18 of system 10 in one embodiment maintainsadditional information such as the individual cost of the supplies, theweight of the supplies, alternatives for the required supplies if thepatient depletes the required supplies during the delivery cycle. Asshown above in FIGS. 7A and 7B, the regimen generation software in oneembodiment uses or generates the weight and cost data as a factor orpotential factor in determining which regimes to be selected asprescriptions. The server of the dialysis center 120 downloads (ortransfers via data card) the alternative solution data to the patient'sAPD Cycler. In one implementation, when a patient starts a therapy, thepatient is notified of the particular solution bag(s) needed for thatday's particular prescription. If the patient runs outs of one type ofsolution, the system can provide an alternative based upon the solutionsheld in current inventory.

Dextrose Mixing

As shown above in FIG. 5, system 10 contemplates using a plurality ofdifferent dextrose levels for each of the different brands or types ofdialysates available, which increases the doctor/clinician's options inoptimizing prescriptions for the patient. The tradeoff with dextrose isgenerally that higher levels of dextrose remove more UF but have highercaloric input, making weight control for the patient more difficult. Theconverse is also true. Dialysate (at least certain types) is provided indifferent standard dextrose levels including 0.5%, 1.5%, 2.5% and 4.25%.As seen in FIG. 6, night dextrose, last fill dextrose and day dextrosecan be chosen to have any of the above standard percentages or to have ablended dextrose level of 2.0%, 2.88%, 3.38% or 3.81%. System 10 usesthe dialysis instrument 104 to mix the standard percentages to createthe blended percentages. It is believed that since each of the standarddextrose level dialysates has been approved by the Federal DrugAdministration (“FDA”) and that the blended dextrose levels are withinapproved levels, that such mixing would meet readily with FDA approval.

TABLE 4 Glucose-Based Solutions Mixing Ratios and Concentrations.Available Dextrose Dextrose Solutions of Concentration MixingConcentration (%) Dextrose (%) Ratio after mixing Concentration (%) 1.5and 2.5  1 to 1 2.00 1.5 1.5 and 4.25 1 to 1 2.88 2.0 2.5 and 4.25 1 to1 3.38 2.5 2.5 and 4.25 1 to 3 3.81 2.88 3.38 3.81 4.25

Using 1:1 or 1:3 mixing shown in Table 4 generates more dextrosesolutions, providing more therapy options for clinicians. At the sametime, these mixing ratios will use all the solution in a container,resulting in no waste.

Referring now to FIG. 14, dialysis instrument 104 illustrates oneapparatus capable of producing the blended dextrose level dialysates.Dialysis instrument 104 is illustrated as being a HomeChoice® dialysisinstrument, marketed by the assignee of the present dialysate. Theoperation of the HomeChoice® dialysis instrument is described in manypatents including U.S. Pat. No. 5,350,357 (“the '357 Patent”), theentire contents of which are incorporated herein by reference.Generally, the HomeChoice® dialysis instrument accepts a disposablefluid cassette 50, which includes pump chambers, valve chambers andfluid flow pathways that interconnect and communicate fluidly withvarious tubes, such as a to/from heater bag tube 52, a drain tube 54, apatient tube 56 and dialysate supply tubes 58 a and 58 b. While FIG. 14shows two supply tubes 58 a and 58 b, dialysis instrument 104 andcassette 50 can support three or more supply tubes and thus three ormore supply bags. Further, as seen in the '357 Patent, one or bothsupply tubes 58 a and 58 b can Y-connect to two supply bags if moresupply bags are needed.

In one embodiment, system 10 pumps fluid from a supply bag (not shown),through one of supply tubes 58 a or 58 b, cassette 50, to a warmer bag(not shown) located on a heater tray of APD 104. The warmer bag providesan area for the solutions to mix before being delivered to the patient.In such a case, the warmer bag can be fitted with one or more conductivestrip, which allows a temperature compensated conductivity reading ofthe mixed solution to be taken to ensure that the solutions have beenmixed properly. Alternatively, APD 104 uses inline heating in which casethe mixing is done inn the tubing and the patients peritoneum.

To operate with cassette 50, the cassette is compressed between acassette actuation plate 60 and a door 62. The '357 Patent describes aflow management system (“FMS”), in which the volume of fluid pumped fromeach pump chamber (cassette 50 includes two in one embodiment), aftereach pump stroke is calculated. The system adds the individual volumesdetermined via the FMS to calculate a total amount of fluid delivered toand removed from the patient.

System 10 contemplates proportioning the pump strokes from differentstandard dextrose supplies using FMS to achieve a desired blendeddextrose. As seen at Table 4 above, a 1.5% standard dextrose supply bagcan be connected to supply line 58 a, while a 4.25% standard dextrosesupply bag is connected to supply line 58 b. Dialysis machine 104 andcassette 50 pump dialysate from each supply bag in a 50/50 ratio usingFMS to achieve the 2.88% blended dextrose ratio. In another example, a2.5% standard dextrose supply bag is connected to supply line 58 a,while a 4.25% standard dextrose supply bag is connected to supply line58 b. Dialysis machine 104 and cassette 50 pump dialysate from eachsupply bag in a 50/50 ratio using FMS to achieve the 3.38% blendeddextrose ratio. In a further example, a 2.5% standard dextrose supplybag (e.g. 2 L bag) is connected to supply line 58 a, while a 4.25%standard dextrose supply bag (e.g. 6 L bag) is connected to supply line58 b. Dialysis machine 104 and cassette 50 pump dialysate from eachsupply bag in a 25/75 (2.5% to 4.25%) ratio using FMS to achieve the3.81% blended dextrose ratio.

The first two examples can include a connection of a six liter bag ofdialysate to each of lines 58 a and 58 b. In the third example, a threeliter 2.5% standard dextrose supply bag is connected to supply line 58a, while a three liter 4.25% supply bag is Y-connected to a six liter4.25% supply bag, which are both in turn connected to supply line 58 b.Dialysis machine 104 and cassette 50 pump dialysate from each supply bagto a heater bag in one embodiment, which allows the two differentdextrose dialysates to fully mix before being pumped from the heater bagto the patient. Accordingly, system 10 can achieve the blended dialysateratios shown in FIG. 6 and others by pumping different standard dextroselevels at different ratios using FMS. Dialysis instrument 104 in oneembodiment is configured to read bag identifiers to ensure that thepatient connects the proper dialysates and proper amounts of thedialysate. Systems and methods for automatically identifying the typeand quantity of dialysate in a supply bag are set forth in U.S. patentapplication Ser. No. 11/773,822 (“the '822 Application”), entitled“Radio Frequency Auto-Identification System”, filed Jul. 5, 2007,assigned to the assignee of the present disclosure, the entire contentsof which are incorporated herein by reference '822 Application disclosesone suitable system and method for ensuring that the proper supply bagsare connected to the proper ports of cassette 50. System 10 canalternatively use a barcode reader that reads a barcode placed on thebag or container for solution type/volume identification. In the case inwhich Y-connections are needed, APD machine 104 can prompt the patientto confirm that the Y-connection(s) to an additional bag(s) has beenmade.

U.S. Pat. No. 6,814,547 (“the '547 patent”) assigned to the assignee ofthe present disclosure, discloses a pumping mechanism and volumetriccontrol system in connection with FIGS. 17A and 17B and associatedwritten description, incorporated herein by reference, which uses acombination of pneumatic and mechanical actuation. Here, volume controlis based on precise control of a stepper motor actuator and a knownvolume pump chamber. It is contemplated to use this system instead ofthe FMS of the '357 Patent to achieve the above blended dextrose levels.

Prescription Download And Therapy Data Upload Communication Module

Referring now to FIG. 15A, network 100 a illustrates one wirelessnetwork or communication module 20 (FIG. 1) for communicating the PET,regimen generation, prescription filtering, inventory tracking, trendingand prescription modification information (below) between patient 102,doctor 110 and dialysis center 120. Here, the patient 102 operates adialysis instrument 104, which communicates wirelessly with a router 106a, which is in wired communication with a modem 108 a. The doctor 110operates the doctor's (or nurse's) computer 112 a (which could also beconnected to a doctor's network server), which communicates wirelesslywith a router 106 b, which is in wired communication with a modem 108 b.The dialysis center 120 includes a plurality of clinician's computers112 b to 112 d, which are connected to a clinician's network server 114,which communicates wirelessly with a router 106 c, which is in wiredcommunication with a modem 108 c. Modems 108 a to 108 c communicate witheach other via an internet 116, wide area network (“WAN”) or local areanetwork (“LAN”).

FIG. 15B illustrates an alternative wired network 100 b or communicationmodule 20 (FIG. 1) for communicating the PET, regimen generation,prescription filtering, inventory tracking, trending and prescriptionmodification information (below) between patient 102, doctor 110 anddialysis center 120. Here, the patient 102 includes a dialysisinstrument 104, which is in wired communication with modem 108 a. Thedoctor 110 operates the doctor's (or nurse's) computer 112 a (whichcould also be connected to a network server), which is in wiredcommunication with a modem 108 b. The dialysis center 120 includes aplurality of clinician's computers 112 b to 112 d, which are in wiredcommunication with a modem 108 c. Modems 108 a to 108 c againcommunicate with each other via an internet 116, WAN or LAN.

In one embodiment, the data points for curve 30 of FIG. 3 are generatedat instrument 104 and sent to either the doctor 110 or the dialysiscenter 120 (most likely dialysis center 102), which houses the softwareto fit curve 30 to the data points. The patient then travels to thedialysis center 120 to have the blood work performed and to complete thePET as discussed herein. The software configured to run the PET andregimen generation screens of FIGS. 4A, 4B, 5A, 5B and 6 can be storedon clinician's server 114 (or individual computers 112 b to 112 d) ofclinic 120 or computers 112 a of doctor 110 (most likely clinic 120) ofsystem 100 a or 100 b. Likewise, software configured to run thefiltering and prescription optimization screens of FIGS. 6A to 6C, 7A,7B, 8A to 8C and 9A to 9E can be stored on server 114 (or individualcomputers 112 b to 112 d) of clinic 120 or computers 112 a of doctor 110of system 100 a and 100 b.

In one embodiment, dialysis center 120 runs the PET, generates theregimens and filters the prescriptions. Dialysis center 120 via network100 (referring to either or both networks 100 a and 100 b) sends theprescriptions to doctor 110. The filtering can be performed with inputfrom the patient via either network 100, telephone or personal visit.The doctor reviews the prescriptions to approve or disapprove. If thedoctor disapproves, the doctor can send additional or alternativefiltering criteria via network 100 back to dialysis center 120 toperform additional filtering to optimize a new set of prescriptions.Eventually, either dialysis center 120 or doctor 110 sends approvedprescriptions to instrument 104 of patient 102 via network 100.Alternatively, dialysis center 120 or doctor 110 stores the approvedprescriptions and instrument 104 and on a given day queries dialysiscenter 120 or doctor 110 over network 100 to determine whichprescription to run. The prescriptions can further alternatively betransferred via a data card.

In an alternative embodiment, dialysis center 120 runs the PET andgenerates the regimens. Dialysis center 120 via network 100 sends theregimens to doctor 110. The doctor reviews the regimens and filters sameuntil arriving at a set of prescriptions to approve or disapprove. Thefiltering can again be performed with input from the patient via eithernetwork 100, telephone or personal visit. If the doctor disapproves, thedoctor can perform additional filtering to optimize a new set ofprescriptions. Here, doctor 110 sends approved prescriptions toinstrument 104 and of patient 102 via network 100. Alternatively, doctor110 stores the approved prescriptions and instrument 104 on a given dayqueries doctor 110 over network 100 to determine which prescription torun. Further alternatively, doctor 110 sends the approved prescriptionsto dialysis center 120 over network 100, the dialysis center stores theapproved prescriptions, and instrument 104 on a given day queriesdialysis center 120 over network 100 to determine which prescription torun.

As discussed above, dialysis center 120 is likely in a better positionto handle the inventory tracking module or software 18 than is doctor110. In one embodiment therefore, dialysis center 120 stores thesoftware configured to run the inventory tracking screens of FIGS. 10 to13. Dialysis center 120 communicates with dialysis instrument 104 andpatient 102 via network 100 to control the inventory for the patient'sapproved prescriptions.

Referring now to FIG. 16A, network 100 c illustrates a furtheralternative network or communication module 20 (FIG. 1). Network 100 calso illustrates the patient data collection module 22 of FIG. 1. Itshould be appreciated that the patient data collection principlesdiscussed in connection with network 100 c are also applicable tonetworks 100 a and 100 b. Network 100 c includes a central clinicalserver 118, which could be stored in one of the dialysis centers 120,one of the doctor's offices 110 or at a separate location, such as at afacility run by the provider of system 10. Each of patients 102 a and102 b, doctors 110 a and 110 b and dialysis centers 120 a and 120 bcommunicates with clinical server 118, e.g., via internet 116. Clinicalserver 118 stores and runs the software associated with any of the PET,regimen generation, prescription filtering, inventory tracking, trendingand prescription modification (below) modules and facilitatescommunication between patients 102 a/102 b, doctors 110 a/110 b anddialysis centers 120 a/120 b.

Clinical server 118 in one embodiment receives the PET data from one ofthe dialysis centers 120 (referring to either of centers 120 a or 120 b)and sends it to clinical server 118. The UF data points of FIG. 3 can besent to clinical server 118 either directly from patient 102 (referringto either of patients 102 a or 102 b) or from dialysis center 120 viapatient 102. Clinical server 118 fits curve 30 (FIG. 3) to the datapoints and generates the regimens and either (i) filters the regimens(perhaps with patient input) into optimized prescriptions for the doctor110 (referring to either of doctors 110a or 10b) to approve ordisapprove or (ii) sends the regimens to doctor 110 to filter intooptimized prescriptions (perhaps with patient input).

In either case, the approved prescriptions may or may not be sent to anassociated dialysis center 120. For example, if the associated dialysiscenter 120 runs inventory tracking module 18 of system 10, the dialysiscenter 120 needs to know the prescriptions to know which solutions andsupplies are needed. Also, if system 10 is operated such that thepatient's dialysis instrument 104 (referring to either instrument 104 aor 104 b) queries the dialysis center 120 on a given day for whichprescription to run, the dialysis center 120 needs to know theprescriptions. Alternatively, the patient's dialysis instrument 104queries clinical server 118 daily for which prescription to run. It isalso possible for clinical server 118 to run inventory tracking module18 of system 10, in which case the associated dialysis center 120 can berelegated to obtaining the PET data.

Clinical server 118 can be a single server, e.g., a national server,which is a logical geographical boundary because different countrieshave different sets of approved dialysis solutions. If two or morecountries have the same approved set of dialysis solutions and a commonlanguage, however, clinical server 118 could service the two or morecountries. Clinical server 118 can be a single server or have spoke andhub links between multiple servers.

Referring now to FIG. 16B, network 100d includes a central clinicalserver 118, which is housed at or close to one of the dialysis centers120. The dialysis center 120 houses a doctor's office 110 and a patientservice area 90, each including one or more computer 112 a to 112 d.Patient service area 90 includes a server computer 114, whichcommunicates with clinical server 118 in one embodiment via a local areanetwork (“LAN”) 92 for the dialysis center 120. Clinical server 118 inturn includes a clinical web server that communicates with internet 116and LAN 92, a clinical data server that communicates with LAN 92 and aclinical database that interfaces with the clinical data server.

Each of patients 102 a and 102 b communicates with clinical server 118,e.g., via internet 116. Clinical server 118 stores and runs the softwareassociated with any of the PET, regimen generation, prescriptionfiltering, inventory tracking, trending and prescription modification(below) modules and facilitates communication between patients 102 a/102b, doctors 110 and dialysis centers 120. Other dialysis centers 120 cancommunicate with center-based clinical server 118 with internet 116. Anyof systems 100 a to 100d can also communicate with an APD machinemanufacturer's service center 94, which can include for example aservice database, a database server and a web server. Manufacturer'sservice center 94 tracks machine problems, delivers new equipment, andthe like.

Data Collection Feature

PET module 12, regimen generation module 14 and prescriptionoptimization or filtering module 16 output data that networks 100(referring now additionally to networks 100 c and 100 d) use to rundialysis therapies. In this regard, the networks 114 and 118 of system10 use results from analysis that have already been performed. As seenwith network 100 c, system 10 also generates real-time daily patientdata, which is fed to a server 114 (of a center 120) or 118 for trackingand analysis. This real-time data, along with therapy parameter inputsand therapy target inputs make up the data collection module 22 (FIG. 1)of system 10.

Each dialysis machine 104 (referring to either or both machines 104a and104b) includes a receiver 122 as illustrated in FIG. 16A. Each receiver122 is coded with an address and a personal identification number(“PIN”). The patient is equipped with a blood pressure monitor 124 and aweigh scale 126. Blood pressure monitor 124 and weigh scale 126 are eachprovided with a transmitter, which sends patient blood pressure data andpatient weight data, respectively, wirelessly to receiver 122 ofdialysis machine 104.

The address and PIN ensure that the information from blood pressuremonitor 124 and weigh scale 126 reaches the proper dialysis machine 104.That is, if machines 104 a and 104 b and associated blood pressuremonitors 124 and weigh scales 126 are within range of each other, theaddresses and PIN's ensure that the dialysis machine 104 a receivesinformation from the blood pressure monitor 124 and weigh scale 126associated with dialysis machine 104a, while dialysis machine 104 breceives information from the blood pressure monitor 124 and weigh scale126 associated with dialysis machine 104 b. The address and PIN alsoensure that dialysis machines 104 do not receive extraneous data fromunwanted sources. That is, if data from an unwanted source is somehowtransmitted using the same frequency, data rate, and communicationprotocol of receiver 122, but the data cannot supply the correct deviceaddress and/or PIN, receiver 122 will not accept the data.

The wireless link between blood pressure monitor 124, weigh scale 126and dialysis machine 104 allows the devices to be located convenientlywith respect to each other in the patient's room or house. That is, theyare not tied to each other via cords or cables. Or, blood pressure andweight data are entered into instrument 104 manually. The wireless linkhowever also ensures that the blood pressure data and weight data, whentaken, are transferred automatically to the dialysis machine 104. It iscontemplated that the patient takes his/her blood pressure and weighshimself/herself before (during or directly after) each treatment toprovide a blood pressure and weight data point for each treatment. Datacollection module 22 is configured alternatively to have a wiredconnection between one or more of blood pressure monitor 124 andinstrument 104 and weight scale 126 and instrument 104.

Another data point that is generated for each treatment is the amount ofultrafiltration (“UF”) removed from the patient. All three data points,blood pressure, patient weight and UF removed, for each treatment can bestored in the memory of the dialysis machine 104, on a data card and/orbe sent to a remote server 114 or 118. The data points are used toproduce performance trends as described next.

Any one or combination of the processing and memory associated with anyof the dialysis instrument 104, doctor's (or nurse's) computer 112,clinician's server 114, clinical web server 118 or manufacturer'sservice center 94 may be termed a “logic implementer”.

Trending And Alert Generation

The trending analysis and statistics module 24 (FIG. 1) of system 10 asseen herein calculates short term and long term moving averages of dailyUF and other patient data shown below. Monitoring actual measured dailyUF, only, produces too much noise due to residual volume and measurementerror of the dialysis instrument 104. The trending regimes of module orfeature 24 therefore look at and trend daily data as well as data thatis averaged over one or more period of time.

Trending and Alert Generation Using Multiple Patient Parameters

Trending module 24 in one embodiment uses the following equation to formthe short term and long term moving averages:UF_(ma)(n)=1/k*(UF(n)+UF(n−1)+UF(n−2) . . . +UF(n−k)). For the shortterm moving average, typical values for k can be, e.g., three tofourteen days, such as seven days. For the long term moving average,typical values for k can be, e.g., fifteen to forty-five days, such astwenty-eight days.

The difference between target UF and actual measured UF is set forth as:

ΔUF=UF _(target) −UF _(ma).

UF_(target) is the physician prescribed UF, and UF_(ma) is the movingaverage value of actual daily UF measured (either daily measured valueor two to three days for short term moving average). ΔUF can be eitherpositive or negative. When the absolute value of ΔUF/UF_(target) exceedsthe alert threshold preset by physician, system 10 (either at themachine 104 level or via server 114/118) alerts the patient and thephysician 110 and/or clinician 120, which can trigger prescriptionadjustment or other response as discussed below.

The alert threshold can account for UF anomalies so as not to makesystem 10 false trigger or be oversensitive to daily UF fluctuations,which can inherently be significant (e.g., due to measurement error andresidual volume). The following equation illustrates an example in whichsystem 10 requires a number of days of a certain UF deviation:

δ(alert generated)=|ΔUF/UF _(target) |>X% for Y days.

X% can be preset by the physician or clinician, and a typical value maybe thirty to fifty percent. Y can also be preset by either the physicianor clinician, and a typical value may be three to seven days.

The next equation addresses the possibility of a patient skippingdialysis or the patient's UF being consistently much lower than thetarget UF:

${{{{If}\mspace{14mu} \delta} = {{\sum\limits_{i = 1}^{q}\delta_{i}} = {\sum\limits_{i = 1}^{q}{\frac{\Delta \; U\; F}{U\; F_{target}}}}}}\rangle}P\% \mspace{14mu} {for}\mspace{14mu} Q\mspace{14mu} {days}$

P% can be preset by the physician or clinician, and a typical value maybe 150% to 250%. Q can also be preset by the physician or clinician, anda typical value can be two to three days. The above equation calculatesa difference between the daily measured UF and the target UF andexpresses the difference as a percentage of the target UF to determinean error in the percentage. Then the error percentage is accumulatedover several days, e.g., two to three (Q) days. If the accumulatederrors exceeds the threshold (P%), system 10 will generate UF alerts.The following examples illustrate the above algorithm:

EXAMPLE #1 P=150%, Q=3 Days

-   Day #1, patient skipped therapy, UF error=100%; Day #2, patient    skipped therapy, UF error=100%; Day #3, patient performed therapy,    UF error=10%; accumulated UF error=210% >150%, then it will generate    alarm.

EXAMPLE #2 P=150%, Q=3 days

-   Day #1, patient skipped therapy, UF error=100%; Day #2, patient    performed therapy, UF error=20%; Day #3, patient performed therapy,    UF error=10%; accumulated UF error=130% <150%, no alarm will be    generated.

FIGS. 17 to 21 show trending screens 128, 132, 134, 136 and 138,respectively, which can be displayed to the patient on a display device130 of the dialysis instrument 104 and/or on a computer monitor atdoctor 110 or dialysis center 120. It is contemplated to generate thetrends at any of these locations as needed. The main trending screen 128of FIG. 17 allows the patient for example to select to see: (i) pulseand pressure trends; (ii) recent therapy statistics; (iii) UF trends;and (iv) weight trends. The patient can access any of the screens via atouch screen input, via a membrane or other type of switch associatedwith each selection, or via knob or other selector that allows one ofthe selections to be highlighted, after which the patient presses a“select” button.

When the patient selects the pulse and pressure trends selection of themain trending screen 128, display device 130 displays the pulse andpressure trends screen 132 of FIG. 18. Pulse (heart rate, line with's), systolic pressure (line with ▴'s), and diastolic pressure (linewith ▪'s) are shown for a one month time period in units of mmHg.Selection options on the pulse and pressure trends screen 132 include(i) returning to the main trending screen 128, (ii) seeing weekly trendsfor pulse, systolic pressure, and diastolic pressure instead and (iii)advancing to the next trending screen 134. The one or more therapyperformed during the trend period, namely, therapy number 1 (e.g.,standard UF), is also shown.

When the patient selects the next trending selection of the pulse andpressure trends screen 132, display device 130 displays a recent therapytrends or statistics screen 134 as seen in FIG. 19. FIG. 19 shows actualvalues for UF removed (in milliliters, including total UF and breakoutUF's for day and night exchanges), cumulative dwell time (in hours,seconds, including total dwell and breakout dwells for day and nightexchanges), drain volume (in milliliters, including total volume andbreakout volumes for day and night exchanges), and filled volume (inmilliliters, including total volume and breakout volumes for day andnight exchanges). The recent therapy trends or statistics screen 134 asseen in FIG. 19 in one embodiment shows statistics from the previoustherapy. Screen 134 alternatively includes a logs option that enablesthe patient to view the same information for prior therapies, e.g.,therapies up to a month ago or since the download of the last set ofprescriptions. Selection options displayed on the recent therapy trendsor statistics screen 134 include (i) returning to the main trendingscreen 128, (ii) returning to the previous trending screen 132 and (iii)advancing to the next trending screen 136.

When the patient selects the next trending selection of the recenttherapy trends or statistics screen 134, display device 130 displays aUF trends screen 136 as seen in FIG. 20. UF trends screen 136 shows atarget UF line and a measured UF line for a one month time period inunits of milliliters. Selection options on the UF trend screen include(i) returning to the main trending screen 128, (ii) seeing weekly trendsfor UF, monthly trends for UF or even three month trends for UF as longas the data is available, and (iii) advancing to the next trendingscreen 138. The therapy performed during the UF trending period is alsoshown..

When the patient selects the next trending selection of the recent UFtrends screen 136, display device 130 displays a patient weight trendsscreen 138 as seen in FIG. 21. Patient weight trends screen 138 shows ameasured body weight (“BW”) line for a one month time period in units ofpounds. Selection options on the patient weight trend screen 138 include(i) returning to the main trending screen 128 and (ii) advancing to anext screen. The therapy performed during the BW trending period is alsoshown.

Alternative trending charts of FIGS. 22 and 23 display actual days onthe x-axis. These trends could be reserved for the doctor 110 and/ordialysis center 120 or be accessible additionally by the patient,meaning that the alerts can be generated from the APD device 104 to thedoctor and/or clinician or from a server computer to the doctor,clinician and/or patient. The trending charts of FIGS. 22 and 23 showthe expected UF value or UF target for the patient based upon thepatient's last PET results. If the difference between expected PET UFand the actual UF increases beyond a value that the doctor or cliniciandetermines is significant, the clinician can order a new PET with orwithout lab work. That is, the patient can perform the UF portion of thePET as described in connection with FIGS. 2 and 3 and bring the drainvolumes to the dialysis center 120. U.S. patent application Ser. No.12/128,385, entitled “Dialysis System Having Automated Effluent SamplingAnd Peritoneal Equilibration Test”, filed May 28, 2008, the entirecontents of which are incorporated herein by reference, discloses anautomated PET, which results in effluent samples that the patient canbring to the dialysis center 120 so that the samples can be analyzed forurea clearance, creatinine clearance, etc.

The trending charts of FIGS. 22 and 23 also show which therapyprescription was used on a given day, as seen at the right side of thefigures (assuming for example that therapy prescription Zero is low UF,therapy prescription One is standard UF and therapy prescription Two ishigh UF). As seen in FIGS. 22 and 23, the standard UF prescription isperformed on all but two days in which the high UF prescription isperformed. In FIG. 22, the thirty day moving average (line with 's) inparticular shows that, generally, the patient's treatment is meeting theUF goal. In FIG. 23, the thirty day moving average (line with 3 s)shows that after about Oct. 2, 2006, the patient's treatment started tonot meet the UF goal and became progressively worse. Moreover, theentire thirty day trend line for October, 2006 slopes towards less andless UF removal. A skilled clinician here will see such trend aspotentially being due to a loss of the patient's renal function andeither (in conjunction with a doctor) order a new PET, provide newprescriptions or put the patient's UF performance on a close watch tosee if the trend continues. The clinician/doctor could alternativelyprescribe that the high UF prescription be performed more frequently inan attempt to reverse the negative-going UF trend.

As alluded to above, the doctor or clinician likely does not want to benotified when a single day falls below the lower limit. The UF datatherefore needs to be filtered. The filter for example considers allthree of daily UF values, the seven day rolling average UF (line with▾'s) and the thirty day rolling average (line with 's) in an algorithmto determine if the patient's prescription needs to be modified. Thefilter can also consider which therapies are being performed. Forexample, alert notification can occur sooner if the patient runs a highpercentage of High UF therapies and is still failing to meet standardtherapy UF targets.

One specific example of an alert algorithm is: the thirty day rollingaverage UF (line with 's) has fallen by ten percent, and the actual UF(base line) has been below the lower limit for three of the past sevendays while performing either the standard UF prescription or the high UFprescription. Another specific example of an alert algorithm is: thethirty day rolling average UF (line with 574 's) has fallen by tenpercent and the seven day rolling average UF (line with ▾'s) has fallenbelow the lower limit LCL, while performing either the standard UF ofthe high UF therapies.

The alert algorithm can also take into account daily weight and bloodpressure data. For example, when the UF deviation, daily blood pressureand body weight each exceed a respective safety threshold, an alert istriggered. In one specific example, system 10 alerts if (i) UF deviatesfrom the target UF; and (ii) short term moving average (e.g., three toseven days) body weight (“BW”) is greater than a threshold; and (iii)short term moving average (e.g., three to seven days) systolic/diastolicblood pressure (“BP”) is greater than a threshold. BP and BW thresholdsare preset by physician 110.

Further, body weight data alone can trigger an alarm when for examplethe patient is gaining weight at a certain rate or gains a certainamount of weight. Here, system 10 can notify the doctor 110 and/orclinician 120, prompting a call or electronic mail to the patient askingfor an explanation for the weight gain. If the weight gain is not due todiet, it could be due to an excessive amount of dextrose in thepatient's prescription, such that a new lower dextrose prescription orset of such prescriptions may need to be prescribed. For example,clinician 120 can set the patient's target body weight, and if the dailymeasured body weight is off by Xw pounds for Yw days in a seven dayperiod, body weight gain is considered excessive and an alert istriggered:

ΔBW=BW _(m) −BW _(target) >Xw for Yw days, where

BW_(m) is the measured daily body weight, BW_(target) is the target bodyweight (set by doctor 110 or clinician 120), Xw is a limit of bodyweight exceeding the target (set by doctor 110 or clinician 120), and Ywis the number of days (set by doctor 110 or clinician 120).

Likewise, an increase in blood pressure alone could prompt acommunication from the doctor 110 and/or clinician 120 for anexplanation from the patient. It is further contemplated to trend thepatient's daily bio-impedance, especially as such sensing comes of age.A bio-impedance sensor can for example be integrated into a bloodpressure cuff (for wired or wireless communication with dialysisinstrument 104), so that such sensing does not inconvenience thepatient. System 10 uses bio-impedance in one embodiment to monitor thedialysate patient hydration state by estimating the patient's intra andextra- cellular water. Such data aids the patient and clinician inselecting a therapy (e.g., high UF when patient is over hydrated and lowUF patient is dehydrated). Bio-impedance can thereby help to control thepatient's fluid balance and blood pressure.

The clinician in the main is concerned about two factors: therapyeffectiveness and patient compliance. Patients whose UF is below targetbecause they are running a low UF therapy too often, or are skippingtherapies, need to be told in a timely manner to change their behavior.Patients whose UF is below target but who are fully compliant and mayeven be performing high UF therapies to obtain their target UF may needto have their prescription(s) changed in a timely manner. The trends ofFIGS. 22 and 23 provide all such information to the clinician.

System 10 knows accordingly if the lower than expected UF is due tocompliance issues or potential therapy prescription issues. In anembodiment in which the patient chooses to pick which prescription torun on a given day, the dialysis instrument 104 can be programmed toprovide a warning to the patient when the patient runs the low UFprescription too often (low UF prescription may be less demanding thanstandard UF prescription). The programming can be configured to escalatethe warnings if the patient continues with this behavior, let thepatient know that the dialysis center 120 is being notified, and notifythe dialysis center accordingly. The instrument 104 can likewise beprogrammed to warn the patient if the patient skips too many treatmentsand notify the dialysis center 120 if the missed treatments continue.Here, the warning and notification can be made regardless of whether thepatient picks the prescription to run or the machine 104/clinic 120chooses the prescriptions on a given day.

FIG. 24 summarizes the options available for setting simple or complexalert generation logics. The parameters that could be monitored include(on the top row): (i) daily UF deviation limit, (ii) UF deviationaccumulation limit, (iii) body weight target and (iv) blood pressurelimit. The middle logic operators show that using one or more of (a)measured daily UF, (b) measured daily body weight, and (c) measureddaily blood pressure, the limits of the top row can be combined indifferent combinations using “AND” logic “OR” Boolean logic to determinewhen to send an alert to the patient, doctor or clinician. Theillustrated, alerts are based on (i) UF and BW or (ii) UF, BW and BP. Analert can be based on UF alone, however.

Referring now to FIG. 25, algorithm or action flow diagram 140 shows onealternative alert sequence for system 10. Upon starting at oval 142,system 10 collects daily UF, BP, and BW data at block 144. At block 146,deviation analysis is performed, e.g., based on doctor/cliniciansettings and rolling averages for UF, BP and BW. At diamond 148, methodor algorithm 140 of system 10 determines whether any of UF, BP, and BWis exceeding limits. If not, method or algorithm 140 waits for anotherday, as seen at block 150, and then returns to the collection step ofblock 144. If one or a combination of limits is exceeded at diamond 148,an alert is sent to the patient, clinician and/or doctor. Deviation andaccumulated values are reset.

A hold or watch period is then started at diamond 154, e.g., for sevendays, to see if the alert condition persists. During this period, it iscontemplated that system 10 communicates between patient 104, doctor 110and/or clinician 120 daily or otherwise regularly until the low UF trendis reversed. The clinician may make suggestions during this period,e.g., to try the high UF prescription or modify the patient's diet. Asdiscussed, dialysis center 120 also receives trending data for patientweight and blood pressure in addition to the UF trending data. The meanarterial pressure (“MAP”) may be the most appropriate value to trendrelative to blood pressure. The clinicians evaluate the weight and MAPdata concurrently during the low UF periods.

If the alert condition persists for a period, e.g., seven days as seenat diamond 154, method 140 and system 10 order a new PET and/or changethe patient's prescription, as seen at block 156. Afterwards, method 140ends as seen at oval 158.

Patient Case Studies

Patient A started peritoneal dialysis treatment just two months ago andstill has residual renal function (“RRF”). His UF target is 800 mL/perday. The doctor set the alert watch to look at daily UF deviation, UFdeviation accumulation and target body weight. Here, a deviation limit Xwas chosen to be 30%, for a period Y equal to four out of seven days. Athree-day UF deviation accumulated error was chosen to be 150%. Targetbody weight was selected to be 240 pounds with a safety limit delta of+five pounds in seven days. The following table is an example of themeasured daily twenty-four hour UF, BP and BW for a seven day period.

TABLE 5 Patient A measured parameters Parameters Sun Mon Tue Wed ThurFri Sat Daily UF (mL) 600 650 700 600 550 750 730 DailySystolic/Diastolic 150/90 148/95 160/97 153/88 165/98 170/95 160/92Pressure (mmHg) Daily Body Weight 242.5 243.3 243.5 244.7 243.1 245.4245.8 (LB) Daily UF Deviation 25% 19% 13% 25%

 6%  9% From Target UF Daily UF Deviation — — 57% 57% 69% 62% 46%Accumulated Error Daily Body Weight 2.5 3.3 3.5 4.7 3.1

Deviation from Target (LB)

In the week of therapy shown above for Patient A, only Thursday's dailyUF falls below the 30% lower limit threshold. The three-day accumulatedUF deviation does not exceed 150%. The patient's body weight stays underlimit (+five pounds) in all but the last two days. Here, system 10 doesnot generate an alert.

Patient B has been on PD for over two years. She is very compliant withher therapy and follows clinician's instructions strictly. She does nothave any RRF and her daily UF target is 1.0 L. Here, the doctor 110and/or clinician 120 set alert conditions as follows. A deviation limitX was chosen to be 20%, for a period Y equal to four out of seven days.A three-day UF deviation accumulated error was chosen to be 150%. Targetbody weight was selected to be 140 pounds with a safety limit delta of+five pounds in seven days. The following table is an example of themeasured daily twenty-four hour UF, BP and BW for a seven day period.

TABLE 6 Patient B measured parameters Sun Mon Tue Wed Thur Fri Sat DailyUF (mL) 880 850 920 870 910 950 930 Daily Systolic/Diastolic 150/93142/86 147/92 153/86 155/90 173/90 166/87 Pressure (mmHg) Daily BodyWeight (LB) 143.5 144.3 143.8 144.3 143.1 144.6 144.8 Daily UF DeviationFrom 12% 15%  8% 13%  9%  5%  7% Target UF Daily UF Deviation — — 35%36% 30% 27% 21% Accumulated Error Daily Body Weight 3.5 3.3 3.8 4.3 3.14.6 4.8 Deviation from Target (LB)

In the week of therapy shown above for Patient B, none of the daily24-hour UF values falls below the 20% lower limit threshold. The 3-dayaccumulated UF deviation does not exceed 150% on any day. The patient'sweight never exceeds the threshold of +five pounds. Accordingly, system10 does not generate a trending alert this week.

Patient C has been on PD for over a year. The patient sometimesover-eats/drinks and skips a therapy from time to time. He does not haveany RRF and his daily UF target is 1.0 L. Here, the doctor 110 and/orclinician 120 set alert conditions as follows. A deviation limit X waschosen to be 25%, for a period Y equal to four out of seven days. Athree-day UF deviation accumulated error was chosen to be 150%. Targetbody weight was selected to be 220 pounds with a safety limit delta of+five pounds in seven days. The following table is an example of hismeasured daily 24-hour UF, BP and WE for a seven day period.

TABLE 7 Patient C measured parameters Sun Mon Tue Wed Thur Fri Sat DailyUF (mL) 880 700 840 900 0 700 500 Daily Systolic/Diastolic 167/97 156/88177/96 163/96 165/90 166/90 178/89 Pressure (mmHg) Daily Body Weight(LB) 223.5 225.3 223.8 224.3 225.1 225.6 225.8 Daily UF Deviation From12%

16% 10%

Target UF Daily UF Deviation — — 58% 56% 126% 140%

Accumulated Error Daily Body Weight 3.5

3.8 4.3

Deviation from Target (LB)

In the week of therapy shown above for Patient C, the patient's daily UFfell below the 25% threshold on Monday, Thursday, Friday and Saturday,as highlighted. The three-day accumulated UF deviation exceeded the 150%limit after Saturday's therapy. The patient also exceeds his +five poundweight limit four times, namely, on Monday, Thursday, Friday andSaturday. System 10 accordingly sends a trending alert after this week.

Trending and Alert Generation Using Statistical Process Control

It is also contemplated to use statistical process control (“SPC”) toidentify instability and unusual circumstances. Referring now to FIG.26, one example moving average or trend is shown, which shows a five-dayaverage UF (triangles) and an average UF for the previous thirty days(base middle line). A range is calculated to be the difference betweenthe lowest and the highest UF values over the past thirty days. An uppercontrol limit (“UCL”, line with X's through it) for a given day iscalculated to be:

UCL=(the moving average for the given day)+(a constant, e.g., 0.577, *the range for the given day)

-   -   and the lower control limit (“LCL”, line with /'s through it) is        calculated to be

LCL=(the moving average for the given day)−(the constant, e.g., 0.577, *the range for the given day).

FIG. 26 shows a UF trend created for a patient, e.g., from August 2003through June 2004 using SPC. In December of 2003 and in April of 2004,the five day moving average UF (triangles) fell below the LCL. System 10could be configured to monitor the five day average and alert thepatient, clinic and/or doctor when the five day moving average UF(triangles) falls below the LCL (or falls below the LCL for a number ofdays in a row). The software configured to generate the trends can belocated at the dialysis instrument 104 or at either server computer 114or 118. In various embodiments, any one or more or all of the patient102, dialysis center 120 or doctor 110 can access and view the trends.The trend data is generated whether or not the trend is actually viewedby anyone. The alerts are auto-generated in one embodiment, enablingsystem 10 to monitor patient 102 automatically for the dialysis center120 and doctor 110.

FIG. 27 shows a second trend for the same patient after changes havebeen made to the patient's prescription. Here, the patient's daytimeexchange has been changed from Nutrineal® dialysate to Extraneal®dialysate. FIG. 27 shows the difference (line with 's) a newprescription had on the patient's UF starting in September of 2004 andagain in November of 2004 when the patient's residual renal functiontapered off.

The statistical process control alert algorithm can also take intoaccount body weight (“BW”) and/or blood pressure (“BP”). If UF has anormal distribution having a mean value of μ and standard deviation of Ccalculated based on time and population, and wherein C is an empiricallydetermined constant. In most processes that are controlled usingStatistical Process Control (SPC), at each time point, multiplemeasurements or observations are made (e.g. measure the room temperaturemultiple times at 8:00 AM), however in one embodiment of system 10, theSPC only one measurement is made at each time point, e.g., one UFmeasurement, one pressure and weight reading per day. System 10 couldthen alert if: (i) short term moving average (e.g., three to seven days)UF is outside the upper control limit (UCL_(UF)=UF_(target)+Cσ) or lowercontrol limit (LCL_(UF)=UF_(target)−Cσ); or (ii) short term movingaverage (three to seven days) body weight>BW threshold; and/or (iii)short term moving average (three to seven days) systolic/diastolic BP>BPthreshold.

FIG. 27 shows that the SPC trending charts can also display the expectedUF value (line with ▴'s) for the patient based upon his/her last PETresults. The thirty-day average line shows that actual UF, while laggingslightly behind expected UF (which is to be expected for a thirty dayaverage) eventually aligns itself with the expected UF results. Here,the patient is not underperforming, he/she is able to meet target. Thepatient may be losing RRF, however, meaning the patient's prescriptionneeds to be more aggressive for UF because the patient has increasingless ability to remove waste and excess water himself/herself. On theother hand, if the difference between expected UF and the actual UFincreases beyond a value that the doctor/clinician determines issignificant, e.g., below LCL for one or more day, the clinician/doctorcan order a new PET as discussed above.

Prescription Recall and Modification

System 10 also includes a prescription recall and modification feature26 shown and described above in connection with FIG. 1. Referring now toFIG. 28, prescription recall and adjustment feature or module 26 isillustrated in more detail. Prescription recall and adjustment featureor module 26 relies on and interfaces with other features of system 10,such as the improved PET feature 12, regimen generation feature 14,prescription filtering feature 16, and trending an alert generationfeature 24. As seen in FIG. 28, one aspect of prescription recall andadjustment feature or module 26 is the selection of one of the approvedprescriptions for treatment.

Referring now to FIG. 29, a screen 160 of display device 130 of dialysismachine 104 illustrates one patient selection screen that allows thepatient to select one of the approved prescriptions (standard, high andlow UF) for the day's treatment. The type of input can be via membranekeys or here via a touch screen overlay, for which areas 162, 164 and166 have been mapped into memory as standard UF prescription, high UFprescription and low UF prescription selections, respectively. System 10in the illustrated embodiment allows the patient to select aprescription for each day's treatment. For example, if the patient hasconsumed more fluids than normal on a given day, the patient can run thehigh UF prescription. If the patient has worked out, been in the sun, orfor whatever reason perspired a great deal during the day, the patientmay choose to run the low UF prescription.

If the patient, e.g., viewing the therapy trend screens 134 and 136above notices a drop in UF running the standard UF prescription, thepatient may be empowered to choose to run the high UF prescription for afew days to see how the patient reacts to the prescription change.Presumably, daily UF will increase. But it should also be appreciatedthat the patient, clinician or doctor should check to see if the actualincreased UF meets the increased expected UF due to the use of the highUF prescription. If the patient is underperforming for bothprescriptions, it may be time for a new PET and possibly a new set ofprescriptions.

Allowing the patient to adjust his/her therapy as described above islikely done only when the doctor or clinician has a comfort level withthe patient that the patient is compliant in terms of lifestyle andadherence to treatment. Also, the doctor/clinician may wish to make surethat the patient has enough experience with the treatment and thedialysis instrument 104 to be able to accurately gauge when the patientneeds a high, versus a low, versus a standard UF treatment. Even thoughthe patient is making the prescription decisions in this embodiment, thedata for trending as shown above is being sent to the dialysis center120 and/or doctor 110, so that if the patient is making poor decisionsas to which prescriptions to run, the dialysis center 120 and/or doctor110 can detect the situation in short order and correct it. For example,system 10 enables the dialysis center 120 and/or doctor 110 to removeprescriptions from possible selection or to set the dialysis instrument104 such that it automatically selects a prescription set at eithermachine 104 or a server computer 114 or 118.

It is believed, given the importance of a dialysis treatment, that mostpeople will be responsible and that a conscientious and thoughtfulpatient will best be able to gauge when the patient may need a moreaggressive or less aggressive prescription, knowing that even the lessaggressive prescriptions have been approved and will remove a certainamount of UF. It is contemplated to provide more than threeprescriptions. For example, the patient can have two high UFprescriptions, one that requires a longer night therapy and the otherthat requires a higher dextrose level. Assuming the patient knows thathe/she needs to run a high UF prescription after a relatively largeliquid intake day, the patient may choose the longer night therapy highUF prescription knowing that he/she has gained weight of late and isbetter off staying away from the higher caloric intake of the higherdextrose prescription. System 10 attempts to accommodate the patient'slifestyle, while ensuring that a proper therapy is performed, while alsogathering therapy data over time to establish a thorough patient historythat enables physiologic changes of the patient to be detectedrelatively quickly and accurately.

In another embodiment, dialysis instrument 104 selects whichprescription the patient is to run based on the patient's daily bodyweight and possibly the patient's blood pressure. The patient weighshimself/herself, a weight signal is transmitted, e.g., wirelessly todialysis instrument 104, which uses the weight signal to determine howmuch UF the patient has accumulated and accordingly which prescriptionto run. In one embodiment, the patient weighs himself/herself just priorto the last nighttime fill or just prior to a mid-day fill to establisha localized “dry weight”. The patient then weighs himself/herself atnight, just after the last fill drain, to establish a localized “wetweight”. The difference between the localized “wet weight” and thelocalized “dry weight” determines a UF amount. The UF amount is fittedinto one of a low UF range, standard UF range and high UF range.Dialysis instrument 104 then picks a corresponding low UF prescription,standard UF prescription or a high UF prescription to run.Alternatively, dialysis instrument 104 provides alternativeprescriptions for a particular range, e.g., two high UF prescriptions,allowing the patient to pick one of the two high UF prescriptions. Asdiscussed above, dialysis instrument 104 is configured in one embodimentto read bag identifiers to ensure that the patient connects the properdialysate(s) and proper amount(s) of dialysate(s).

In a further alternative embodiment, the doctor 110 or dialysis center120 chooses or pre-approves the prescriptions to be run on a given day,such that the patient does not have the ability to run a differentprescription. Here, fill, dwell, and/or drain times can be preset, andthe dialysis instrument 104 can also be configured to read bagidentifiers to ensure that the patient connects the proper dialysate(s)and proper amount(s) of dialysate(s).

It is further contemplated to allow the patient to have input into whichprescriptions are run, but wherein the doctor 110 or dialysis center 120ultimately approves of a prescription selection or selection plan beforethe prescriptions are downloaded into dialysis instrument 104. Forexample, it is contemplated that the dialysis center 120 send anauto-generated email to the patient 102, e.g., each month one week priorto the start of the next month. The email includes a calendar, each dayof the calendar shows all available prescriptions, e.g., (i) lowUF, (ii)midUFlowDEX, (iii) midUFhighDEX, (iv) highUFlowDEX and (v)highUFhighDEX. The patient clicks on one of the prescriptions for eachday and sends the completed calendar to clinician 120 for approval. Forexample, the patient may choose to run one of the high UF prescriptionson the weekends and one of the middle or standard prescriptions duringthe week. Perhaps the patient attends physically demanding workoutclasses after work on Mondays and Wednesdays and selects the low UFprescription for those days.

It is contemplated to allow the patient to type notes in the days toexplain why a particular prescription has been proposed. For example,the patient could select the lowUF prescription and type “spin class” inthat calendar day. Or the patient could select the highUFhighDEXprescription and type “birthday party, up early next day” in thatcalendar day.

When the clinician receives the completed, proposed calendar from thepatient, the clinician can either approve of the proposed calendar, callor email the patient with questions as to why one or more prescriptionwas chosen for a particular day, forward the calendar to the doctor'soffice 110 if the clinician is concerned or has questions regarding theproposed calendar, or modify the selections in the calendar and send themodified calendar back to the patient. The clinician can review thepatient's trend data when evaluating the proposed prescription calendar.For example, if the patient has been gaining weight and has selected thehigh dextrose standard UF for many or all of the days of the month, theclinician can call or email the patient and suggest switching to the lowdextrose standard UF prescription in an attempt to control the patient'sweight gain.

Eventually, the clinician and the patient reach a consensus. The doctormay or may not need to be consulted. It is expected that the patient'scalendars will look similar from month to month and may change naturallybased on season, vacations, and holidays. When a more radical change ispresented, e.g., the patient intends to start a vigorous workout ortraining routine and wants to introduce more low UF days, the cliniciancan seek doctor approval.

In one embodiment, dialysis instrument 104 confirms that the patientwants to run a non-standard treatment on a particular day. The dialysisinstrument 104 also enables the patient to switch to a standard therapyif the patient desires. For example, if the patient has Mondays andWednesdays approved for a low UF prescription because the patientexpects to have vigorous workouts those days, but the patient skips aworkout, the patient can choose to run a standard UF prescriptioninstead. Or, if the patient is slated to run a high UF prescriptionbecause he/she is supposed to attend a party on a given day, but missesthe party, the patient can choose to run a standard UF prescriptioninstead.

Dialysis instrument 104 could also be configured to provide the patientwith a limited amount of prescription changes from a standard UFprescription to a low or high UF prescription. For example, if thepatient decides to workout Thursday instead of Wednesday, the patientcould switch the prescription from standard UF to low UF on Thursday.System 10 could be configured to allow for one such standard tonon-standard prescription change per week, for example.

In another example, dialysis instrument 104 allows the patient toincrease the UF removal at any time, that is, switch from low UFprescription to a standard or high UF prescription, or switch from astandard UF prescription to a high UF prescription at any time. If thepatient chooses this option a certain number of times during the month,dialysis instrument 104 can be configured to send an alert to the doctor110 or clinician 120.

The approved calendar in one embodiment is integrated with the inventorytracking feature 18. The approved calendar tells the inventory trackingfeature 18 what is needed for the next delivery cycle, which can be amonth to month cycle. If the patient can plan and gain approval formultiple months, the delivery cycle can be for the multiple months. Inany case, the patient can be delivered extra solution if needed to allowfor switches from the planned prescriptions.

In a further alternative embodiment, the patient and clinician and/ordoctor agree that for each week the patient will run a certain number ofstandard, low and high prescriptions, e.g., five standard, one low andone high. The patient then chooses which days during the week to run thevarious prescriptions. The weekly allotment does not have to include anylow UF or high UF allotments. The patient could have seven standard UFallotments with four low dextrose standard and three high dextrosestandard prescriptions, for example. Here too, dialysis instrument 104can be configured to let the patient change prescriptions in certaininstances as described above.

In still a further alternative embodiment, dialysis instrument 104 orone of the server computers 114 or 118 picks one of the approvedprescriptions for the patient for each therapy. The pick can be based onany of the trending data above and/or based on a series of questionsanswered by the patient such as: (i) Was your fluid intake today low,moderate, average, high or very high? (ii) Was your food intake todaylow, moderate, average, high or very high? (iii) Was your carbohydrateintake today low, moderate, average, high or very high? (iv) Was yoursugar intake today low, moderate, average, high or very high? (v) Wasyour activity level today low, moderate, average, high or very high?System 10 then picks one of the approved prescriptions for the patient.Inventory management for this embodiment can be based on average usagesover the past X number of delivery cycles. In any of the above regimes,dialysis instrument 104 can also read bag identifiers to ensure that thepatient connects the proper dialysate(s) and proper amount(s) ofdialysate(s).

As seen in FIG. 28, the selected daily prescriptions are fed into aswitch mechanism for prescription recall and modification feature 26.The switch mechanism is activated when the applied alert generationalgorithm of feature 24 computes an error that is greater than thethreshold(s) of the applied alert generation algorithm. As seen in FIG.28, when the applied alert generation algorithm of feature 24 computesan error that is not greater than the threshold, feature 26 maintainsthe current set of prescriptions and whichever prescription recallregime is being employed. Accordingly, the switch mechanism does notswitch to a new prescription or set of prescriptions.

When a prescription is used for a treatment, the prescription carrieswith it a predicted UF, which is generated via regimen generationfeature 14 and selected via prescription filtering feature 16. Thepredicted UF is also effected by the patient's regimen deviation as seenat summer 64 of the transfer function of FIG. 28

Actual UF data is obtained from the short term and long term movingaverages as discussed above in connection with trending and alertgeneration feature 24, which are in turn developed from measured UF datagenerated at dialysis instrument 104. Actual UF values area function ofthe patient's transport characteristics as has been described herein butalso account for environmental factors, such as regimen deviation by thepatient. Actual UF values are subtracted from the predicted UF values atdifference generator 66 a and fed into the alert generation algorithm atdiamond 24. The actual UF values are also fed into a differencegenerator 66 b, which are used to adjust target UF values used togenerate the regimens in connection with feature 14. Other target valuesinclude target urea removal, target creatinine removal and targetglucose absorption as discussed above..

As seen at diamond 24, once system 10 determines an alarm condition,system 10 triggers prescription adjustment switch mechanism. That doesnot necessarily mean that the patient's prescriptions will be adjusted.The doctor 110 ultimately makes the call based on the data of UF,patient daily weight, daily blood pressure, or estimated dry weightusing a bio-impedance sensor. When it appears prescription adjustment isneeded, system 10 via communications module 20 communicates with thepatient, e.g., via wireless communication between APD system to modemthrough a router. Based on the received data, the nephrologist 110 atswitch mechanism 26 could make following decisions: (i) continue withcurrent prescriptions and come to office visit as previously planned;(ii) continue with current prescriptions but visit office sooner forpossible prescription adjustment; (iii) switch to a different routineusing current prescriptions, visit office soon, e.g., within two weeks,receive trending data on new routine; (iv) warn the patient that he/sheis not being compliant with treatment and maintain currentprescription(s); (v) warn the patient that he/she is running a low UFprescription(s) too often and maintain current prescription(s); (vi)continue with the current therapy and monitoring, but lower the UFtarget to A and lower the UF limit to B; and (vii) perform a new APD PETto evaluate the change of PD membrane transport characteristics andprovide the center with updated therapy suggestions based upon this PET.

If the patient is fully compliant and the low UF is as a result oftransport characteristic changes as verified by the new PET, doctor 110can order a new one or prescription be generated, including a change inone or more standard UF prescription. To do so, regimen generationmodule 14 and prescription filtering module 16 are used again to developthe new prescription(s). The doctor agrees to the new prescriptions andswitch mechanism 26 changes to the new prescription(s) as seen in FIG.28.

It should be understood that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications can be madewithout departing from the spirit and scope of the present subjectmatter and without diminishing its intended advantages. It is thereforeintended that such changes and modifications be covered by the appendedclaims.

1. A peritoneal dialysis (“PD”) system comprising: a logic implementerconfigured to: (i) accept at least one therapy target input; (ii) acceptat least one patient transport characteristic input; (iii) accept atleast one solution input; (iv) accept at least one fluid volume input;(v) accept at least therapy time input; and (vi) generate a pluralitytherapy regimens using the inputs of (ii), (iii), (iv) and (v) thatsatisfy the at least one therapy target input.
 2. The peritonealdialysis system of claim 1, which includes an automatic peritonealdialysis (“APD”) machine operable to run a PD therapy according to aselected one of the therapy regimens.
 3. The peritoneal dialysis systemof claim 2, the logic implementer located at a location remote from theAPD machine.
 4. The peritoneal dialysis system of claim 1, at least oneof the inputs (i) to (v) including an input range/threshold.
 5. Theperitoneal dialysis system of claim 4, the logic implementer configuredto generate each of the therapy regimens possible within the at leastone input range/threshold.
 6. The peritoneal dialysis system of claim 4,which includes a target therapy input range/threshold, the targettherapy input range/threshold including at least one of: (a) a minimumurea clearance; (b) a minimum urea Kt/V; (c) a minimum creatinineclearance; (d) a maximum glucose absorption; and (f) a minimumultrafiltration removal amount.
 7. The peritoneal dialysis system ofclaim 4, which includes a solution input range/threshold, the solutioninput range threshold including a plurality of percentage dextrosesettings for the solution.
 8. The peritoneal dialysis system of claim 7,the solution input range/threshold including the plurality of percentagedextrose settings for at least one of: (a) a night fill, (b) a lastnight fill and (c) a day fill.
 9. The peritoneal dialysis system ofclaim 4, which includes a fluid volume range/threshold, the fill volumerange/threshold including at least one of: a total volume minimum, atotal volume maximum, an individual fill volume minimum and anindividual fill volume maximum.
 10. The peritoneal dialysis system ofclaim 9, the at least one volume minimum and volume maximum applicableto at least one of: (a) a night fill, (b) a last night fill, and (c) aday fill.
 11. The peritoneal dialysis system of claim 4, which includesat least one therapy time range/threshold, the therapy timerange/threshold including at least one of: a minimum therapy time and amaximum therapy time.
 12. The peritoneal dialysis system of claim 11,the at least one therapy time minimum and therapy time maximumapplicable to at least one of (a) a night fill and (b) a day fill. 13.The peritoneal dialysis system of claim 1, the patient transportcharacteristic including at least one of: (a) a mass transfer areacoefficient (“MTAC”) for urea, (b) a MTAC for creatinine, (c) a MTAC forglucose, (d) a patient hydraulic permeability, LPA coefficient, and (e)a fluid absorption characteristic.
 14. The peritoneal dialysis system ofclaim 1, wherein the at least one solution input includes a selectionfrom a plurality of different solution types.
 15. The peritonealdialysis system of claim 14, the solution input entered for at least oneof a last fill and a day fill.
 16. A peritoneal dialysis (“PD”) systemcomprising: a logic implementer configured to: (i) accept at least onetherapy target input; (ii) accept at least one patient transportcharacteristic input; (iii) accept at least one solution input; (iv)accept at least one fluid volume input; (v) accept at least one therapytime input, at least one of the inputs (ii) to (v) including an inputrange; and (vi) generate each therapy regimen using the inputs of (ii),(iii), (iv) and (v), including each of the possibilities within the atleast one input range, that satisfy the at least one therapy targetinput.
 17. The peritoneal dialysis system of claim 16, which includes asolution dextrose range for solution input (iii), a minimum and maximumindividual fill volume range for fluid volume input (iv), and a minimumand maximum therapy time range for therapy time input (v).
 18. Theperitoneal dialysis system of claim 17, which further includes a rangeof selectable solution types for solution input (iii) and a minimum andmaximum total fill volume for fluid volume input (iv).
 19. Theperitoneal dialysis system of claim 16, wherein at least one of theranges is applicable to at least two of a night fill, a night last fill,and a fill.
 20. A peritoneal dialysis system comprising: a logicimplementer configured to: (i) accept a plurality of therapy targetinputs; (ii) accept a plurality of patient transport inputs; (iii)accept one of a range of solution inputs; (iv) accept one of a range offluid volume fill inputs; (v) accept one of a range of fill time inputs;and (vi) generate each possible therapy regime using the therapy inputsof (ii) and each of possibilities of the ranges of (iii) to (v) thatmeets each of the therapy target inputs of (i).
 21. The peritonealdialysis system of claim 20, wherein the therapy target inputs aretherapy target thresholds.
 22. The peritoneal dialysis system of claim20, the logic implementer configured to auto-input a possibility from atleast one of the input ranges.
 23. The peritoneal dialysis system ofclaim 22, the logic implementer further configured to override the atlease one auto-inputted possibility with a patient-selected possibility.